Systems and methods for filtering medical device noise artifacts from venous waveform signals

ABSTRACT

Devices, systems, and methods for filtering medical device noise artifacts from circulatory waveform signals are disclosed. A circulatory pressure is measured and transformed from the time domain to the frequency domain for analysis to determine patient status. To avoid artifacts of the pumping, the time-domain measurements are filtered to generate a filtered time-domain signal, by removing active pumping periods. The filtered time-domain signal is transformed into a frequency-domain signal, which is analyzed based upon peaks indicating respiratory rate, heart rate, or harmonics thereof. Peaks may be adjusted based on a ratio that considers removed signals. A metric of patient status is then determined from the peaks or corresponding frequencies. The patient status may be related to blood volume of the patient and may be used to control pump operation.

PRIORITY CLAIM

This application claims priority to U.S. Provisional Application No.62/599,421, entitled “Systems and Methods for Filtering Medical DeviceNoise Artifacts from Venous Waveform Signals”, filed Dec. 15, 2017, andto U.S. Provisional Application No. 62/671,108, entitled “Systems andMethods for Monitoring and Determining Patient Parameters from SensedVenous Waveform,” filed May 14, 2018, the entire contents of which areincorporated herein by reference and relied upon.

BACKGROUND

Conventional methods of establishing blood volume and related metricsindicative of patient condition have relied upon highly invasivemeasurements of central venous pressure (herein “CVP”) or other invasivemeasures, such as Swan-Ganz catheterization. Such invasive measurementsrequire the insertion of a catheter specifically for the purpose ofmeasuring blood pressure within the central portion of the patient'scirculatory system. In addition to being highly invasive, the insertionof a catheter solely for the purpose of pressure monitoring increasesthe complexity of treatment and raises the risk of complications, suchas infection. Additionally, CVP measurements may be slower to change inresponse to certain acute conditions, as the circulatory system attemptsto compensate for blood volume disequilibrium (particularly hypovolemia)by protecting blood volume levels in the central circulatory system atthe expense of the periphery. For example, constriction in peripheralblood vessels may reduce the effect of fluid loss on the central system,thereby masking blood loss for a period of time in conventional CVPmeasurements. Such masking can lead to delayed recognition and treatmentof patient conditions, resulting in worse patient outcomes.

To address the issues associated with CVP measurements, the use ofperipheral intravenous analysis (herein “PIVA”) has been developed, asdescribed in U.S. patent application Ser. No. 14/853,504 (filed Sep. 14,2015 and published as U.S. Patent Publication No. 2016/0073959) and PCTApplication No. PCT/US16/16420 (filed Feb. 3, 2016, and published as WO2016/126856). Such PIVA techniques measure peripheral venous pressure(herein “PVP”) using intravenous (herein “IV”) lines, such as IV tubingattached to a saline drip or IV pump. In addition to utilizing existingIV lines, the PIVA techniques also include transformation of the PVPmeasurements into the frequency domain to identify a respiratory ratefrequency (F₀) equal to the respiratory rate of the patient and a heartrate frequency (F₁) equal to the heart rate of the patient. Although thePIVA techniques previously disclosed provide an excellent indication ofheart rate and blood volume status in certain situations, the disclosureherein further improves upon the previously disclosed PIVA techniques toaddress challenges related to other situations, improve accuracy,provide earlier warnings of potential problems, or identify additionalpatient conditions.

Monitoring patient metrics during dialysis or other pumping presents aparticular challenge to both conventional and PIVA methods. Inparticular, pumping blood into a patient circulatory system generates ahigh level of (pressure variation induced) noise related to the pumpingcycle. Measured signal values associated with such noise during pumpingperiods may be orders of magnitude larger than signal values associatedwith non-pumping periods. Existing techniques for monitoring patientmetrics under such conditions involve either shutting down the pump foran extended period or attempting to remove the primary effect of thepump from the measured pressure. Shutting down the pump for extendedperiods during treatment may be infeasible where consistent pumping isneeded, such as during surgery. Even where feasible, such approach canstill result in substantial delays in determining the patient statusbecause of the need to interrupt pumping in to obtain measurements.Similarly, existing techniques that attempt to remove the primary effectof the pump address only the principal artifacts introduced by the pumpand are sensitive to errors in estimates of the primary effect of thepump. Such techniques also typically require a priori informationregarding the operation of the pump (e.g., the amplitude and frequencyof pressure waves generated by the pump), and some such techniquesfurther require additional information regarding precise timing of thephases of the pump cycle. Such techniques produce only crude estimatesof pressure, which estimates are unsuitable for PIVA or other advancedmetrics of patient status. Specifically, such techniques at best removeonly approximations of the primary artifacts of pump operation, whileleaving numerous secondary artifacts in the measured pressure signal.Moreover, such techniques are dependent upon accurate estimates of theprimary pumping artifacts and are sensitive to any errors in theestimates, such as errors caused by variation in pump operation overtime. The techniques described herein represent a means of avoiding therespective problems of both types of existing techniques.

Accordingly, systems and methods are needed to filter medical devicenoise artifacts from venous waveform signals.

SUMMARY

In light of the disclosure herein, and without limiting the scope of theinvention in any way, in a first aspect of the present disclosure, whichmay be combined with any other aspect listed herein unless specifiedotherwise, a system for monitoring a patient using a measurementassociated with a pressure within a circulatory system of the patientwhile the circulatory system of the patient is connected to a pumpincludes a pressure sensor and an evaluation unit. The pressure sensorincludes a transducer disposed adjacent to or connected to a tube influid connection with the circulatory system and configured to generatean electronic signal associated with the pressure while the circulatorysystem of the patient is connected to the pump. The evaluation unitincludes a computer processor communicatively connected to the pressuresensor to receive the electronic signal and a memory storingnon-transitory computer-readable instructions that, when executed by thecomputer processor, causes the evaluation unit to obtain a time-domainpressure signal. The time-domain pressure signal includes values of anelectronic signal associated with the pressure from the transducer basedupon a physical phenomenon associated with the pressure of the patientover a sample period. The sample period includes a plurality of timesegments, including (i) one or more active time segments during whichthe pump is operating and (ii) one or more inactive time segments duringwhich the pump is not operating. The evaluation unit identifies a firstplurality of the values of the time-domain pressure signal associatedwith the one or more inactive time segments and a second plurality ofthe values of the time-domain pressure signal associated with the one ormore active time segments via forward and backward slope detection ofthe time-domain pressure signal. The evaluation unit deletes the secondplurality of values of the time-domain pressure signal and generates afiltered time-domain pressure signal based upon the first plurality ofthe values and excluding the second plurality of the values. Theevaluation unit applies a transformation to the filtered time-domainpressure signal to generate a frequency-domain pressure signal/ Theevaluation unit adjusts the frequency-domain pressure signal, based on aratio between a size of the second plurality of values of thetime-domain pressure signal and a total sample window of values of thetime-domain pressure signal. The evaluation unit determines a patientstatus metric for the patient based upon the adjusted frequency-domainpressure signal.

In a second aspect of the present disclosure, which may be combined withany other aspect listed herein unless specified otherwise, the pump is aperistaltic IV pump.

In a third aspect of the present disclosure, which may be combined withany other aspect listed herein unless specified otherwise, the pump isconfigured to operate periodically, such that the one or more activetime segments and the one or more inactive time segments periodicallyalternate.

In a fourth aspect of the present disclosure, which may be combined withany other aspect listed herein unless specified otherwise, the tube isdisposed between the patient and the pump such that a part of the pumpis in fluid connection with the circulatory system of the patient viathe tube.

In a fifth aspect of the present disclosure, which may be combined withany other aspect listed herein unless specified otherwise, thetransducer comprises a pressure sensor disposed in fluid connection withan interior of the IV tube, and the physical phenomenon associated withthe pressure is a pressure within the interior of the IV tube.

In a sixth aspect of the present disclosure, which may be combined withany other aspect listed herein unless specified otherwise, theevaluation unit further determines whether the patient status metricindicates a condition of the patient is abnormal, and adjusts operationof the pump when the patient status metric indicates the condition ofthe patient is abnormal by changing a rate of flow of a fluid from thepump into the circulatory system of the patient

In a seventh aspect of the present disclosure, which may be combinedwith any other aspect listed herein unless specified otherwise, theevaluation unit generates the filtered time-domain pressure signal by,for each of one or more pairs of the active time segments, identifyingone or more corresponding values within both of the active time segmentsof the pair, and combining the active time segments of the pair byaligning the one or more corresponding values within both of the activetime segments of the pair.

In a eighth aspect of the present disclosure, which may be combined withany other aspect listed herein unless specified otherwise, theevaluation unit generates the filtered time-domain pressure signal byestimating a third plurality of values as substitute values for the oneor more active time segments, where the third plurality of values areestimated based upon the first plurality of values without reference tothe second plurality of values, and generating the filtered time-domainpressure signal by combining the first plurality of values for theinactive time segments and the third plurality of values for the activetime segments.

In a ninth aspect of the present disclosure, which may be combined withany other aspect listed herein unless specified otherwise, the thirdplurality of values are estimated by performing at least one ofregression analysis, forward-backward slope calculation, two-sided slopedetection, and mirror matched filtering on at least the first pluralityof values.

In a tenth aspect of the present disclosure, which may be combined withany other aspect listed herein unless specified otherwise, theevaluation unit determines the patient status metric by identifying aplurality of frequencies associated with local maxima of thefrequency-domain pressure signal, and determining the patient statusmetric based at least in part upon at least one of the plurality offrequencies associated with the local maxima.

In a eleventh aspect of the present disclosure, which may be combinedwith any other aspect listed herein unless specified otherwise, thepatient status metric is a blood volume metric indicating one or more ofthe following: hypovolemia, hypervolemia, or euvolemia.

In a twelfth aspect of the present disclosure, which may be combinedwith any other aspect listed herein unless specified otherwise, a devicefor monitoring a patient includes a pressure sensor and an evaluationunit. The pressure sensor includes a transducer configured to monitor aphysical phenomenon associated with a pressure within a circulatorysystem of the patient while the circulatory system of the patient isconnected to a pump. The evaluation unit includes a computer processorcommunicatively connected to the pressure sensor and a memory storingnon-transitory executable instructions that, when executed by thecomputer processor, cause the evaluation unit to obtain a time-domainpressure signal comprising values of an electronic signal associatedwith the pressure received from the transducer of the pressure sensorover a sample period. The sample period includes a plurality of timesegments, including (i) one or more active time segments during whichthe pump is operating and (ii) one or more inactive time segments duringwhich the pump is not operating. The evaluation unit identifies a firstplurality of the values of the time-domain pressure signal associatedwith the one or more inactive time segments and a second plurality ofthe values of the time-domain pressure signal associated with the one ormore active time segments via forward and backward slope detection ofthe time-domain pressure signal. The evaluation unit deletes the secondplurality of values of the time-domain pressure signal. The evaluationunit generates a filtered time-domain pressure signal based upon thefirst plurality of the values and excluding the second plurality of thevalues. The evaluation unit applies a transformation to the filteredtime-domain pressure signal to generate a frequency-domain pressuresignal. The evaluation unit adjusts the frequency-domain pressuresignal, based on a ratio between a size of the second plurality ofvalues of the time-domain pressure signal and a total sample window ofvalues of the time-domain pressure signal. The evaluation unitdetermines a patient status metric for the patient based upon theadjusted frequency-domain pressure signal.

In a thirteenth aspect of the present disclosure, which may be combinedwith any other aspect listed herein unless specified otherwise, thetime-domain pressure signal comprises a first time series of discretevalues, the filtered time-domain pressure signal comprises a second timeseries of discrete values, and the second time series contains at leastone segment of a sequential plurality of values within the second timeseries that are equivalent to a corresponding segment of a sequentialplurality of corresponding values within the first time series.

In a fourteenth aspect of the present disclosure, which may be combinedwith any other aspect listed herein unless specified otherwise, theevaluation unit generates the filtered time-domain pressure signal byestimating a third plurality of values as substitute values for the oneor more active time segments, where the third plurality of values areestimated based upon the first plurality of values without reference tothe second plurality of values, and generating the filtered time-domainpressure signal by combining the first plurality of values for theinactive time segments and the third plurality of values for the activetime segments.

In a fifteenth aspect of the present disclosure, which may be combinedwith any other aspect listed herein unless specified otherwise,adjusting the frequency-domain pressure signal includes dividingmagnitudes of the frequency-domain pressure signal by a quotient of thesecond plurality of values and the total sample window of values.

In a sixteenth aspect of the present disclosure, which may be combinedwith any other aspect listed herein unless specified otherwise, a methodof monitoring a patient using a measurement associated with a pressurewithin a circulatory system of the patient while the circulatory systemof the patient is connected to a pump includes monitoring, by atransducer, a physical phenomenon associated with the pressure of thepatient over a sample period. The sample period includes a plurality oftime segments, including (i) one or more active time segments duringwhich the pump is operating and (ii) one or more inactive time segmentsduring which the pump is not operating. The method includes obtaining,by a processor of an evaluation unit, a time-domain pressure signalcomprising values of an electronic signal associated with the pressurefrom the transducer based upon the monitored physical phenomenon overthe sample period. The method includes identifying, by the processor ofthe evaluation unit, a first plurality of the values of the time-domainpressure signal associated with the one or more inactive time segmentsand a second plurality of the values of the time-domain pressure signalassociated with the one or more active time segments via forward andbackward slope detection of the time-domain pressure signal. The methodincludes deleting the second plurality of values of the time-domainpressure signal. The method includes generating, by the processor of theevaluation unit, a filtered time-domain pressure signal based upon thefirst plurality of the values and excluding the second plurality of thevalues. The method includes applying, by the processor of the evaluationunit, a transformation to the filtered time- domain pressure signal togenerate a frequency-domain pressure signal. The method includesadjusting the frequency-domain pressure signal, based on a ratio betweena size of the second plurality of values of the time-domain pressuresignal and a total sample window of values of the time-domain pressuresignal. The method includes determining, by the processor of theevaluation unit, a patient status metric for the patient based upon theadjusted frequency-domain pressure signal.

In a seventeenth aspect of the present disclosure, which may be combinedwith any other aspect listed herein unless specified otherwise,generating the filtered time-domain pressure signal includes removingthe one or more active time segments from the time-domain pressuresignal.

In a eighteenth aspect of the present disclosure, which may be combinedwith any other aspect listed herein unless specified otherwise,generating the filtered time-domain pressure signal includes estimatinga third plurality of values as substitute values for the one or moreactive time segments, where the third plurality of values are estimatedbased upon the first plurality of values without reference to the secondplurality of values, and generating the filtered time-domain pressuresignal by combining the first plurality of values for the inactive timesegments and the third plurality of values for the active time segments.

In a nineteenth aspect of the present disclosure, which may be combinedwith any other aspect listed herein unless specified otherwise, thethird plurality of values are estimated by performing at least one ofregression analysis, forward-backward slope calculation, two-sided slopedetection, and mirror matched filtering on at least the first pluralityof values.

In a twentieth aspect of the present disclosure, which may be combinedwith any other aspect listed herein unless specified otherwise,adjusting the frequency-domain pressure signal includes dividingmagnitudes of the frequency-domain pressure signal by a quotient of thesecond plurality of values and the total sample window of values.

Additional features and advantages of the disclosed devices, systems,and methods are described in, and will be apparent from, the followingDetailed Description and the Figures. The features and advantagesdescribed herein are not all-inclusive and, in particular, manyadditional features and advantages will be apparent to one of ordinaryskill in the art in view of the figures and description. Also, anyparticular embodiment does not have to have all of the advantages listedherein. Moreover, it should be noted that the language used in thespecification has been principally selected for readability andinstructional purposes, and not to limit the scope of the inventivesubject matter.

BRIEF DESCRIPTION OF THE FIGURES

Understanding that the figures depict only typical embodiments of theinvention and are not to be considered to be limiting the scope of thepresent disclosure, the present disclosure is described and explainedwith additional specificity and detail through the use of theaccompanying figures. The figures are listed below.

FIG. 1A illustrates a block diagram of an exemplary PIVA system for usein measuring, analyzing, and responding to a patient's peripheral venousblood pressure, the system having a fluid source.

FIG. 1B illustrates a block diagram of an exemplary PIVA system for usein measuring, analyzing, and responding to a patient's peripheral venousblood pressure, the system not having a fluid source.

FIG. 1C illustrates a block diagram of an exemplary PIVA system for usein measuring, analyzing, and responding to a patient's peripheral venousblood pressure, the system including a sensor disposed within aperipheral vein.

FIG. 1D illustrates a block diagram of an exemplary PIVA system for usein measuring, analyzing, and responding to a patient's peripheral venousblood pressure, the system including a pump.

FIG. 2A illustrates a block diagram of an exemplary PIVA device forimplementing some functions of the exemplary PIVA system, showing afluid connection via a spur of an IV tube.

FIG. 2B illustrates a block diagram of an exemplary PIVA device forimplementing some functions of the exemplary PIVA system, showing afluid connection via a capped IV tube.

FIG. 2C illustrates a block diagram of an exemplary PIVA device forimplementing some functions of the exemplary PIVA system, showing asensor disposed adjacent to an exterior wall of an IV tube.

FIG. 3 illustrates a flow diagram of an exemplary PIVA measurement andanalysis method for measuring and analyzing a patient's peripheralvenous blood pressure.

FIG. 4A illustrates an exemplary plot of time-domain representation of aPVP signal.

FIG. 4B illustrates an exemplary plot of frequency-domain representationof a PVP signal.

FIG. 5A illustrates an exemplary plot of time-domain representation of aPVP signal during operation of a noise-creating medical device.

FIG. 5B illustrates an exemplary plot of time-domain representation ofthe PVP signal after removing active time segments during which themedical device is operating.

FIG. 5C illustrates an exemplary plot of time-domain representation of afiltered PVP signal including estimates of values for the removed activetime segments.

FIG. 6 illustrates a flow diagram of an exemplary pressure signalfiltering method for removing noise artifacts related to operation of amedical device from a signal corresponding to a patient's peripheralvenous blood pressure.

FIG. 7 illustrates an exemplary PIVA comparison method for identifyingchanges in a patient status based upon comparison of PVP over time.

FIG. 8 illustrates a block diagram of exemplary processing performed byan exemplary PIVA module.

FIG. 9 illustrates a block diagram of an exemplary PIVA system,including a PIVA module.

FIG. 10 illustrates a block diagram of exemplary processing performed byan exemplary PIVA module.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

Although the following text sets forth a detailed description ofnumerous different embodiments, it should be understood that the legalscope of the invention is defined by the words of the claims set forthat the end of this patent. The detailed description is to be construedas exemplary only and does not describe every possible embodiment, asdescribing every possible embodiment would be impractical, if notimpossible. One of ordinary skill in the art could implement numerousalternate embodiments, which would still fall within the scope of theclaims. Unless a term is expressly defined herein using the sentence “Asused herein, the term ‘______’ is hereby defined to mean . . . ” or asimilar sentence, there is no intent to limit the meaning of that termbeyond its plain or ordinary meaning. To the extent that any term isreferred to in this patent in a manner consistent with a single meaning,that is done for sake of clarity only, and it is not intended that suchclaim term be limited to that single meaning. Finally, unless a claimelement is defined by reciting the word “means” and a function withoutthe recital of any structure, it is not intended that the scope of anyclaim element be interpreted based on the application of 35 U.S.C. §112(f).

PIVA System and Signal Noise

FIGS. 1A-D illustrate block diagrams of embodiments of an exemplary PIVAsystem 100 for use in measuring, analyzing, and responding to peripheralvenous blood pressure of a patient 102. The exemplary PIVA system 100 ora similar system may be used to implement the various techniques formonitoring patient status based upon measurements associated with PVPfor the patient 102. The PIVA system 100 may measure a pressure signalassociated with the patient's peripheral vein, analyze the pressureusing PIVA techniques to identify key frequency components of thepressure signal, and analyze the key frequency components of thepressure signal to determine patient status based upon one or moremetrics, as discussed below.

The exemplary PIVA system 100 illustrated in FIG. 1A includes an IV tube104 in fluid connection with the circulatory system of the patient 102.Specifically, a venous access device 106 may be inserted into aperipheral vein 108 of the patient 102 at an access point. The venousaccess device 106 may include a needle, catheter, cannula, or othermeans of establishing a fluid connection between the IV tube 104 and theperipheral vein 108. The venous access device 106 may be a separatecomponent connected to the IV tube 104 or may be formed as an integralportion of the of the IV tube 104. In either case, the venous accessdevice 106 may include a terminal end inserted into the peripheral vein108 at the access point and a connecting end that connects to a primaryportion of the IV tube 104. The primary portion of the IV tube 104 mayserve as a conduit between the venous access device 106 and a fluidsource 110.

At some point along the primary portion of the IV tube 104, a pressuresensor 112 may be disposed to monitor a physical phenomenon associatedwith PVP of the patient 102. In some embodiments, the pressure sensor112 may directly measure a pressure corresponding to the PVP, such as apressure in the interior of the IV tube 104. In such embodiments, ameasuring portion of a pressure transducer (e.g., a Piezoelectricpressure transducer) may be disposed in fluid connection with theinterior of the IV tube 104. The pressure sensor 112 may thus also be influid connection with the peripheral vein 108 of the patient through theIV tube 104 and the venous access device 106. The pressure sensor 112 isthereby enabled to measure pressure changes in the peripheral venoussystem of the patient 102 based upon changes in the fluid pressurewithin the IV tube 104.

It should be noted that while pressure sensor 112 disclosed hereintypically is in fluid connection with the peripheral vein 108 of thepatient through IV tube 104 and venous access device 106, it should beappreciated that pressure sensor 112 may alternatively or additionallybe disposed in fluid connection with other anatomical locations, such asother veins (e.g., central venous access locations), other arteries, orboth. More generally, the signal acquisition and analysis disclosedherein could be applicable with a number of different anatomicallocations beyond peripheral vein locations, such as central venousaccess locations via a central venous catheter.

In other embodiments, the pressure sensor 112 may indirectly measure apressure corresponding to the PVP of the patient 102 by measuring otherphenomena, without being disposed in fluid connection with the interiorof the IV tube 104. For example, the pressure sensor 112 may instead beattached to the exterior of the IV tube 104 and thereby disconnectedfrom the interior of the IV tube 104 or the fluid of the fluid source110 (as illustrated in FIG. 2C). In one embodiment, pressure sensor 112is configured within a hinged c-clamp, such that pressure sensor 112 canbe clipped directly onto the IV tube 104. This embodiment preferablyallows for ease of use regarding attachment. The hinged c-clamp mayinclude additional features, such as signal conditioning circuitry.Hinged c-clamp may be wired or wireless. In a wireless configuration,hinged c-clamp may further include internal power supply and externaltransmission capabilities, such as WiFi, Bluetooth, or other wirelesstransmission protocol. Once placed on the IV tube 104, pressure sensor112 continuously acquires the force/pressure from the surface of the IVtube 104, and transmits pressure readings accordingly. Beyond fluidstatus calculations, as discussed in greater detail herein, pressurereadings could be used to calculate heart rate, heart rate variability,arrhythmias, respiratory rate, IV infiltration and/or catheterdislodgement, IV drop counters, IV bag emptying, and a number of otherphysiological and non-physiological parameters.

The pressure sensor 112 may, in some such embodiments, measure pressurebased upon acoustic or optical phenomenon at the sensor location. Insome embodiments, the pressure sensor 112 may be disposed at aterminating end (i.e., a capped off end) of an IV tube 104 insertedspecifically for the purpose of measuring pressure within the peripheralvein 108, in a manner similar to that illustrated in FIG. 1B. In furtherembodiments, other sensors may be used instead of the pressure sensor112, such as sonic, electrical, temperature, or similar sensors tomeasure one or more of the following physical phenomena: pressure,sound, electrical resistivity or conductivity, electrical voltage orcurrent, light levels or properties, or other similar phenomena.Whichever types of sensors are used, the sensors may be (but need notbe) in fluid contact with the peripheral vein 108 of the patient throughthe IV tube 104 and the venous access device 106 (or directly throughthe venous access device 106) to measure the phenomena associated withthe PVP of the patient 102. In yet further embodiments, the sensor 112may be disposed within a portion of a needle, catheter, or other venousaccess device 106 that is inserted within the peripheral vein 108 of thepatient 106, as illustrated in FIG. 1C. Thus, the PVP may be measured insitu within the peripheral vein 108. Such in situ measurement isadvantageous inasmuch as it obviates the effect of temperature,viscosity, and other factors on transmission of pressure within the IVtube 104.

In various embodiments, the pressure sensor 112 may be positioned atvarious distances from the access point of the peripheral vein 108, froma location within the peripheral vein 108 or a location proximate to theconnecting end of the venous access device 106 to a position proximateto the fluid source 110 or at a terminating end of the IV tube 104. Thepressure sensor 112 is illustrated in FIG. 1A as being at anintermediate location along the length of the IV tube 104 in order toillustrate better the various components of the PIVA system 100. In someembodiments, the pressure sensor 112 may directly measure fluid pressurewithin the IV tube 104. Specifically, the pressure sensor 112 mayinclude a transducer that provides an electronic pressure signalindicative of the pressure detected by the transducer to an analysiscomponent 114 via a connection 122. The electronic pressure signal maybe an analog electrical signal directly provided by the transducer ormay be a preprocessed digital signal indicating pressure values basedupon the transducer interface with the primary portion of the IV tube104. In embodiments in which the pressure sensor 112 is not in fluidconnection with the IV tube 104 or the peripheral vein 108, the pressuresensor 112 may nonetheless include one or more transducers to generateelectronic signals associated with the PVP. For example, the pressuresensor 112 may use one or more microphones disposed to detect sound atan exterior surface of an IV tube 104 to generate electronic pressuresignals indicative of pressure within the IV tube 104 as a proxy for PVPwithin the peripheral vein 108.

The analysis component 114 is communicatively connected to the pressuresensor 112 to receive the electronic pressure signal via the connection122. The analysis component 114 may include general-purpose orspecial-purpose processing hardware, such as microprocessors orspecial-purpose analysis circuits. As shown, the analysis component 114may include one or more units for performing the PIVA analysis. Aresponse unit 116 may identify and control responses based upon thepressure data from the pressure sensor 112. The response unit 116 maycontrol the presentation of alarms or may control the operation of thefluid source 110, such as by controlling the rate of fluid flow. Todetermine appropriate responses, the response unit 116 may receiveevaluation data from an evaluation unit 118, which may include metricsdetermined from the electronic pressure signal. The evaluation unit 118may obtain pressure values (or signal values directly or indirectlyassociated with PVP) from the electronic pressure signal and evaluatethe pressure values to determine information regarding the patient 102,such as blood volume metrics or other metrics as described in furtherdetail below. The information generated by the evaluation unit 118 mayalso be stored or presented for patient monitoring. In alternativeembodiments, additional, fewer, or alternative units may be included.For example, the evaluation unit 118 may perform the functions ascribedto the response unit 116 herein.

The analysis component 114 may be communicatively connected to a monitor120 via a connection 126 in some embodiments. The monitor 120 may be aseparate monitor for displaying information regarding the patient or maybe incorporated into another device, such as a pump or other fluidsource device. The monitor 120 may also be communicatively connected tothe fluid source 110 via a connection 128 to receive and displayinformation associated with the fluid source 110. In some embodiments,the monitor 120 may be used to control the operation of the fluid source110, such as by adjusting fluid flow rate, duration of operation, modeof operation, etc. The analysis component 114 may similarly becommunicatively connected to the fluid source 110 via connection 124 insome embodiments. The analysis component 114 may receive informationregarding operation of the fluid source 110 for use in evaluating thepatient by the evaluation unit 118. The response unit 116 may alsocommunicate with the fluid source 110 to control operation of the fluidsource 110 in response to information regarding the patient determinedbased upon the electronic pressure signal from the pressure sensor 112.

In some embodiments, the fluid source 110 may comprise a pump 111, asillustrated in FIG. 1D. Such pump may be disposed within the exemplaryPIVA system 100 to pump blood or other fluids into the peripheral vein108 of the patient 102. For example, the pump 111 may include an IVinfusion pump or a dialysis pump, such as a peristaltic pump. The pump111 may be configured to operate cyclically in a periodic or aperiodicmanner, having alternating intervals of operation (i.e., active timesegments) and rest (i.e., inactive time segments). By alternating thepump 111 between operating and rest intervals, periods of time in whichthe pump 111 is not operating may be used for PIVA analysis, asdescribed further below. In some embodiments, such as where the pump 111is a hemodialysis pump, the pump 111 may further be connected to thecirculatory system of the patient 102 by an additional IV tube 105(which may include or be further attached to an additional venous accessdevice 107), thereby creating an extracorporeal blood circuit throughthe pump 111 via the tubes 104 and 105. In such embodiments, the pump111 may draw blood out of the patient 102 through either of tubes 104 or105. The extracorporeal blood may then be processed according to atherapeutic regimen before being returned to the patient circulatorysystem (or may be replaced by another fluid that may be infused into thepatient circulatory system) through the other of the IV tubes 105 or104. Although described herein as one component, it should be understoodthat the pump 111 may comprise a plurality of pumping components (e.g.,a pair of pumps for extracting and returning blood or other fluids, ormultiple pumps in a common fluid system) in some embodiments.

The various connections 122, 124, 126, and 128 may each be wired orwireless connections in various embodiments. Moreover, some or all ofthe connections 122, 124, 126, and 128 may be internal to devices, suchas a PIVA device 130 or a PIVA-integrated fluid source 140. The PIVAdevice 130 may incorporate the pressure sensor 112 and analysiscomponent 114 (along with associated connections) into a device that maybe attached to or within the IV tube 104 to perform PIVA monitoring ofthe patient 102. The PIVA-integrated fluid source 140 may include acomputer-controlled fluid reservoir or pump configured to utilize PIVAmonitoring of the patient 102 in controlling fluid flow Like the PIVAdevice 130, the PIVA-integrated fluid source 140 may include thepressure sensor 112 and analysis component 114, along with the fluidsource 110 and the monitor 120 (along with associated connections).Alternative embodiments may include additional, fewer, or alternativecomponents in alternative configurations.

FIGS. 2A-C illustrate block diagrams of exemplary embodiments of a PIVAdevice 130 for implementing some functions of the exemplary PIVA system100. As illustrated in FIG. 2A, the exemplary PIVA device 130 may beconfigured to attach to a spur 104A of the IV tube 104, such as at onebranch of a Y-connector or a T-connector. Alternatively, the exemplaryPIVA device 130 may be configured to attach to a terminal end of the IVtube 104, as illustrated in FIG. 2B. In such embodiments, the PIVAdevice 130 may cap a terminating portion of the an IV tube 104, suchthat no fluid source 110 is connected to the peripheral vein 108 throughthe same IV tube 104. Of course, a fluid source could be otherwiseconnected to provide fluids to the patient 102 via another IV tube andanother venous access device. In further embodiments, the PIVA device130 may be configured to attach to the exterior of the IV tube 104, asillustrated in FIG. 2C. In such embodiments, one or more sensors of thePIVA device 130 may monitor PVP without being in fluid connection withthe peripheral vein 106 or the interior of the IV tube 104.

As discussed above, the PIVA device 130 may include a pressure sensor112 disposed such that a sensing portion is in contact with fluid in theIV tube 104, as illustrated in FIGS. 2A-B. In some embodiments, thepressure sensor 112 (or an alternative sensor) may instead be externalto the IV tube 104, as illustrated in FIG. 2C. However situated, thepressure sensor 112 is disposed to monitor a physical phenomenonassociated with pressure in the peripheral vein 108. Such physicalphenomenon may include pressure in the IV tube 104, expansion orcontraction of the IV tube 104, sound in the IV tube 104, vibrations ofthe IV tube 104, or other similar phenomena. The pressure sensor 112 maybe electrically communicatively connected to a microprocessor 132 via asystem bus 138. The microprocessor 132 (MP) may be furthercommunicatively connected to a program memory 134 and a communicationunit 136 (COMM UNIT) via the system bus 138. The program memory 134 maybe a non-transitory, non-volatile memory (e.g., a flash memory) storingexecutable instructions that may be executed by the microprocessor 132to evaluate the electronic pressure signal from the pressure sensor 112,determine patient information (e.g., blood volume metrics), determineappropriate responses to the determined patient information, and controlthe communication unit 136 to electronically communicate with the fluidsource 110 or monitor 120 via connections 124 or 126. The program memory134 may store a plurality of routines, scripts, or modules correspondingto units or sub-units of the analysis component 114, such as softwaremodules corresponding to response unit 116 or the evaluation unit 118.

The communication unit 136 may be a hardware component configured tosend and receive electronic data between the PIVA device 130 and thefluid source 110 or monitor 120 via connections 124 or 126. Theconnections 124 and 126 are illustrated as being wired connections inthe exemplary PIVA device 130, which may also be used to obtain powerfor the PIVA device 130. Alternatively, another power connection orbattery (not shown) may provide power to the PIVA device 130. Althoughshown as separate wired connections, the connections 124 and 126 may beseparate or combined wired or wireless connections. The connections 124and 126 may communicate with a communication component of the fluidsource 110 or monitor 120, which may include or be part of a pump 111.Such communications may include raw data generated by the pressuresensor 112, processed data related to measurements by the pressuresensor 112, data analyzed according to the methods described below, oralert signals or control commands determined based upon analyzed data.The fluid source 110 or monitor 120 may then take appropriate action orpresent appropriate information based upon the communications from theexemplary PIVA device 130.

FIG. 3 illustrates a flow diagram of an exemplary PIVA measurement andanalysis method 300 for measuring and analyzing a status of a patient102 based on PVP using the PIVA system 100. The method 300 may be usedto determine various patient status metrics, such as metrics related topatient blood pressure, blood volume, respiration, position or movement,or systemic vascular resistance. The method 300 may be performed by theevaluation unit 118 using an electronic pressure signal from thepressure sensor 112, the generation of which electronic pressure signalby the pressure sensor 112 may be included in the method 300 in someembodiments.

The method 300 begins with measuring a PVP data signal for the patient102 (block 302). The PVP data signal may be measured by using atransducer of the pressure sensor 112 to generate an electronic pressuresignal indicating PVP based upon a physical phenomenon associated withPVP. For example, this may be accomplished by measuring the pressurewithin the IV tube 104. Because the IV tube 104 is in fluid connectionwith the peripheral vein 108 of the patient 102 via the venous accessdevice 106, the pressure in the IV tube 104 measured by the pressuresensor 112 is associated with patient PVP (i.e., the pressure in theperipheral vein 108). In some embodiments of the system PIVA 100, thepressure within the IV tube 104 may be different from the PVP within theperipheral vein 108, but the pressure measured within the IV tube 104may nonetheless be proportional to the PVP in the peripheral vein 108.Thus, the measured PVP data signal may be adjusted to compensate fordifferences between the pressures, if desired. For example, adjustmentsmade be made based upon temperature, viscosity of the patient's blood ora fluid provided by the fluid source 110, or a gauge or rigidity of theIV tube 104. Whether adjusted or unadjusted, the PVP data signalmeasured by the pressure sensor 112 accurately represents changes inpressure over time, including both periodic pressure changes associatedwith respiratory and circulatory cycles and aperiodic pressure changesthat may be indicative of changes in patient condition. Similarly, a PVPdata signal generated by the pressure sensor 112 by components not influid contact with the interior of the IV tube 104 likewise provides arepresentation of the pressure within the peripheral vein 108 of thepatient 102. The PVP data signal may be the electronic pressure signalgenerated by the pressure sensor 112 or may be a data signal derivedtherefrom. In alternative embodiments, the PVP data signal may beevaluated in real-time as it is generated, or it may be stored for lateranalysis. Depending upon the components used to measure the PVP-relatedphenomenon, the PVP data signal may be generated or stored as an analog(i.e., as a continuous function or curve over a time segment) or adigital signal (i.e., as a set of discrete values representing distincttimes).

FIG. 4A illustrates an exemplary chart of a time-domain representationof the PVP data signal, which may be the electronic pressure signal fromthe pressure sensor 112. The chart illustrates a time-domain PVP signal402, which shows periodic increases and decreases in pressure associatedwith the patient heartbeat. Additionally, the time-domain PVP signal 402exhibits slower cyclical variation as a result of patient respiration.The chart also illustrates a respiration curve 404 that shows the effectof inspiration and expiration on the time-domain PVP signal 402. Becauseof the expansion of the lungs during inspiration, the measured pressurein the peripheral vein is higher during inspiration than duringexpiration, when the volume of the lungs is reduced. Other factorsinfluence PVP, such as blood volume and patient movement. Thetime-domain PVP signal 402 is thus a combination of a plurality ofinfluences, both periodic (e.g., heart rate or respiration) andaperiodic (e.g., movement or blood loss). Because the resultingtime-domain PVP signal 402 will include noise from various sources, itmay be difficult to detect small changes in pressure that may serve asindications of patient status. Therefore, PIVA techniques utilize afrequency-domain evaluation of the PVP data signal, as described below.It should be recognized that, although the time-domain representation ofthe PVP data signal is illustrated graphically as a chart in FIG. 4A toillustrate the salient features of the data, it is not necessary toproduce a chart or other graphical representation of such data signal.Instead, in some embodiments, the PVP data signal is processed by theevaluation unit 118 without generating a graphical representation of thetime-domain PVP data signal, or the graphical representation may begenerated for user review separately from evaluation.

Returning to FIG. 3, a plurality of data values may then be obtainedfrom the measured PVP data signal (block 304). The evaluation unit 118may sample values of the live or stored PVP data signal to obtain theplurality of data values. In some embodiments, the data values may besampled at fixed intervals over a period of time to obtain a pluralityof data values within an evaluation window, which may include storingthe plurality of data values associated with the window in temporary orpermanent electronic data storage. In further embodiments, data formultiple evaluation windows may be obtained, such that each evaluationwindow includes a plurality of data values. For example, concurrent timeperiods may be identified as separate evaluation windows, or evaluationwindows may be identified as time periods separated by an interveningperiod (e.g., twenty-second evaluation windows beginning every minute,thus separated by forty-second intervening periods). When the evaluationunit 118 samples values of a live (continuously updating) PVP datasignal, in some embodiments, the evaluation window may be updated on arolling basis to obtain new data values while covering time periods offixed duration. For example, the evaluation window may be repeatedlyupdated by adding new sample data values and removing the oldest sampledata values to maintain a window of a fixed duration (five seconds, tenseconds, twenty seconds, etc.) of the most recent PVP data from thepressure sensor 112. Where the evaluation unit 118 periodically obtainsupdates of new sample data values, the window may be updated (and thetransformation and evaluation described below may be performed for theupdated window) every time a new data value is received. In analternative embodiment, the plurality of data values may correspond tothe continuous values of an analog PVP data signal, which may beobtained and analyzed by analog electronic equipment (which may be partof the evaluation unit 118).

From the plurality of data values, the evaluation unit 118 generatesfrequency-domain data corresponding to the plurality of data values(block 306). This may include applying a data transformation to theplurality of data values representing a time-domain PVP signal toproduce a frequency-domain representation of the PVP signal. In apreferred embodiment, the evaluation unit 118 applies a fast Fouriertransform (FFT) to the sampled plurality of data values. The FFT may beapplied periodically (e.g., every ten seconds, every minute, or everytwo seconds, with or without overlapping evaluation windows). Otheranalysis techniques that can identify local maxima according tofrequency are contemplated, such as wavelet transform, autocorrelation,or other signal analysis techniques that can segregate contributions tosignal spectral energy content over time-domain segments. Thefrequency-domain data may include a plurality of values representing themagnitude of various frequency components in the measured PVP datasignal based upon the plurality of data values. Such values may bediscrete or may be part of a curve of magnitudes corresponding tofrequencies, which curve may be generated by interpolation orapproximation between a finite number of values associated with a finitenumber of frequencies. Although FFT algorithms may be used to greateffect, other time-frequency transforms or other techniques of analyzingfrequency components of signals may be utilized to evaluate theplurality of data values. For example, in addition to other Fouriertransforms, the evaluation may include wavelet transforms ortime-frequency representations of the measured PVP data signal.

FIG. 4B illustrates an exemplary chart of a frequency-domainrepresentation of the PVP data signal, corresponding to the time-domainPVP signal 402 represented in the time domain in FIG. 4A. The chartillustrates the magnitude of each frequency component by a frequencycurve 406. As is customary, the horizontal axis represents frequency,and the vertical axis represents magnitude. Although the chart isexemplary, certain typical features may be discerned therein. Ofparticular interest are the several peaks (P_(N)) of the frequency curve406 associated with frequencies (F_(N)). Between the peaks, minorvariations in magnitude are seen, which may represent minor componentsof the time-domain PVP signal 402 associated with noise in the system orartifacts of the circulatory system of the patient 102 (e.g., movementsof the patient during measurement, or openings and closings of theatrioventricular and aortic valves) or in the exemplary PIVA system 100.Although the frequency-domain representation of the PVP data signal isillustrated in FIG. 4B as a chart to illustrate the salient features, itshould be understood that it is not necessary to produce a chart orother graphical representation of the frequency-domain data. Indeed, inpreferred embodiments, no such graphical representation is generated.Instead, the frequency-domain data is processed by the evaluation unit118 as an intermediate process, the results of which are not directlypresented to a user of the system or device. In some embodiments, thefrequency-domain data may be stored in transitory or non-transitorymemory as values within a data list, data table, or similar datastructure.

Under ordinary conditions, the peak (P₀) with the lowest frequency (F₀)corresponds to the respiration rate of the patient 102, and the peak(P₁) with the next-lowest frequency (F₁) corresponds to the heart rateof the patient 102. One or more harmonic peaks (P_(H)) associated withharmonic frequencies (F_(H)) of the heart rate frequency (F₁) may beidentified in some embodiments. Such harmonic peaks (P_(H)) areassociated with local maxima of the frequency curve 406. The next twopeaks (P₂) and (P₃) of the frequency curve 406 are harmonic peaks(P_(H)) occurring at frequencies associated with the first and secondharmonics of the heart rate at the first harmonic frequency (F₂) and thesecond harmonic frequency (F₃). The harmonics occur at fixed multiplesof the heart rate frequency (F₁), which multiples are typically integermultiples. Specifically, experimental data indicate that first harmonicfrequency (F₂) is approximately twice the heart rate frequency (F₁), andthe second harmonic frequency (F₃) is approximately thrice the heartrate frequency (F₁). Although not shown, additional peaks associatedwith third and higher harmonics of the heart rate may be identified insome embodiments. The further harmonic frequencies (F₄, F₅, . . . F_(N))typically occur at corresponding sequential integer multiples of theheart rate frequency (F₁). Although some variation exists in theobserved frequencies of the peaks associated with the harmonicfrequencies, the harmonic frequency peaks have been found to occur atfrequencies that are typically within a range of approximately tenpercent (i.e., ±10%) of the value of the heart rate frequency above orbelow the integer multiples of the heart rate frequency (F₁). Therelationships between the magnitudes of the peaks (P_(N)) may vary, butthe magnitude of the peak (P₁) associated with the heart rate frequency(F₁) should be greater than the magnitudes of the peaks (P₂), (P₃),etc., associated with the harmonic frequencies (F₂), (F₃), etc.,thereof.

Turning again to FIG. 3, the evaluation unit 118 further identifies aplurality of frequencies (F_(N)) corresponding to peaks (P_(N)) of thefrequency-domain representation of the PVP signal (block 308), such asthe frequency curve 406. The evaluation unit 118 may first identifyvalues indicating peaks (P_(N)) in the frequency-domain representationof the PVP signal by comparison of the frequency-domain PVP signalvalues, then identify the corresponding frequencies (F_(N)) associatedwith the identified peak values (P_(N)). To determine the peak values(P_(N)), the evaluation unit 118 may utilize any of various methods toidentify local maxima as peaks, including methods based upon any or allof a comparison of the relative magnitudes of local maxima,establishment of fixed or dynamic frequency bands around each peak, orcomparison of full width at half maximum for local maxima. For example,a band-pass filter may be employed to separate segments of thefrequency-domain representation of the PVP signal to further identifylocal maxima. This may be particularly useful in identifying harmonicpeaks (P_(N)) and corresponding harmonic frequencies (F_(H)) becausesuch harmonics occur at integer multiples of the heart rate frequency(F₁). As an example, a band-pass filter centered around a frequencytwice the heart rate frequency (F₁) and having a band width of twentypercent of the heart rate frequency (F₁) may be used to define a rangeof the frequency-domain representation of the PVP signal that containsthe first harmonic peak (P₂). The first harmonic frequency (F₂) may thenbe identified by simply determining the frequency associated with thelocal maximum value of the frequency-domain representation of the PVPsignal within such range. By employing these or other known techniques,the peaks (P_(N)) of the frequency-domain representation of the PVPsignal may be distinguished from other local maxima arising from noiseor other minor phenomena in the circulatory system.

Once the plurality of frequencies (F_(N)) associated with the peaks(P_(N)) have been identified, the evaluation unit 118 may analyze themagnitudes of the frequency-domain representation of the PVP signal atone or more of the frequencies (F_(N)) to determine one or more aspectsof patient status (block 310). Such analysis may include determining oneor more patient status metrics, such as a blood volume metric,respiratory volume metric, patient movement metric, systemic vascularresistance metric, other metric relating to the systemic vascularresistance (e.g., mean arterial pressure, mean venous pressure, cardiacoutput), or the like for the patient 102. For example, the patientstatus metrics may include a blood volume metric indicating one of thefollowing hemodynamic states of the patient 102: hypovolemia,hypervolemia, or euvolemia. Hemodynamic states of the patient 102 may bedetermined as a score or as a category of patient status in variousembodiments.

Some patient status metrics may be determined directly from the one ormore frequencies (F_(N)) or magnitudes of the frequency-domainrepresentation of the PVP signal associated therewith. For example,respiratory depth may be determined based upon the magnitude associatedwith the respiratory frequency (F₀) (i.e., the magnitude of therespiratory peak (P₀)), or a blood volume metric may be determined basedupon the magnitude associated with the heart rate frequency (F₁) (i.e.,the magnitude of the heart rate peak (P₁)). As another example, a bloodvolume metric indicative of patient hemodynamic state (e.g., hypovolemiaor hypervolemia) may be directly measured as a magnitude or change inmagnitude of a harmonic peak (P_(H)) associated with a harmonicfrequency (F_(H)), such as a change from a magnitude at a prior timewhen the patient's hemodynamic state was known (e.g., a baselinemeasurement prior to surgery). As yet another example, heart ratevariability may be determined based upon changes in the heart ratefrequency (F₁) over time or by measuring the width of a portion of thefrequency-domain representation of the PVP signal associated with theheart rate peak (P₁) (e.g., the full width at half maximum).

In some embodiments, the patient status metric may be determined basedupon a comparison of magnitudes associated with different frequencypeaks (F_(N) and F_(M)) based upon the same plurality of data values(i.e., for the same evaluation window). For example, a ratio of themagnitudes associated with heart rate and first harmonic frequencies F₁and F₂ may be used to determine a hemodynamic metric for the patient,such as a systemic vascular resistance or a blood volume score. Suchratios may be particularly useful in normalizing magnitudes associatedwith harmonic frequencies (F_(H)) to obtain more robust and moreaccurate patient status metrics. Similarly, ratios between magnitudes ofthe frequency-domain representation of the PVP signal associated withdifferent harmonic frequencies (e.g., F₂ and F₃) may be used todetermine hemodynamic state of the patient 102 (e.g., blood volume). Infurther embodiments, the patient status metric may be determined basedupon a comparison of magnitudes associated with the same one or morefrequencies (F_(N)) of peaks (P_(N)) determined for differentpluralities of data values (i.e., for different evaluation windows). Forexample, analysis of a change in the absolute or relative magnitudeassociated with heart rate frequency F₁ over time may be used todetermine a hemodynamic metric. Information regarding the patient statusmay be stored in a memory, presented to a user via the monitor 120, orused by the response unit 116 to generate and implement a response(e.g., presenting an alarm or controlling the operation of the fluidsource 110), including any of the responses discussed further below.

In further embodiments, additional information regarding the patient maybe used in determining some patient status metrics, or such additionalinformation may be monitored for use with the patient status metrics.For example, information regarding a patient position or movement (e.g.,a patient movement metric) may be separately monitored to providecontext for the patient status metric or to supplement the patientstatus metric. To this end, additional patient metrics may be separatelymonitored by additional sensors 150 collecting data regarding positionsor movements of the patient 102. Such additional patient metrics may beevaluated to verify appropriateness of responses to changes in a patientstatus metric. Thus, if a patient status metric indicates a possibilityof a transient condition at the same time as an additional patientmetric indicates a patient movement, the patient status metric may bedetermined to be the result of the patient movement, so no response maybe required. Alternatively, if the additional patient metric confirms apatient status metric that indicates a patient movement or exertion fora patient who requires ambulatory assistance, an alert may be generatedto warn responsible personnel that that patient may be attempting towalk without assistance. In some embodiments, the additional informationmay include information indicating a patient condition or limitations,such as patient condition information entered by a physician or nurse.

FIGS. 5A-C illustrate exemplary charts of time-domain representations ofa PVP signal that includes noise artifacts, such as from operation of apump 111 or other fluid source 110. The exemplary charts illustratevarious stages or types of processing that may be performed by theanalysis component 114. FIG. 5A illustrates a PVP data signal 502 thatincludes both inactive segments 502I associated with inactive timesegments during which the pump 111 is not operating and active segments502A associated with active time segments during which the pump 111 isoperating. To show the effect of pump activity on the PVP data signal502, FIG. 5A further illustrates operation of the pump 111 by chartingthe pump control signal 504 on the same time scale. The pump controlsignal 504 is illustrated for simplicity as a binary signal, with avalue of “1” indicating active pumping and a signal of “0” indicatinginactivity. In alternative embodiments, however, alternative types ofpump control signals may be used to control the power or mode ofoperation of the pump 111.

As illustrated in FIG. 5A, the pump 111 is not operating during thefirst inactive time segment between times t₀ and t₁, so the values ofthe PVP signal 502 during this time segment form an inactive-pump PVPsignal 502I. The inactive-pump PVP signal 502I represents PVPmeasurements corresponding to pressure in the circulatory system of thepatient 102 without interference from the pump 111. As such, theinactive-pump PVP signal 502I is similar to the time-domain PVP signal402, described above. Thus, the values of the inactive-pump PVP signal502I may be used to perform further analysis according to PIVA or otherfrequency-domain methods, as discussed herein. As further illustrated,the pump 111 is operating during the first active time segment betweentimes t₁ and t₂, immediately following the first inactive time segment.The values of the PVP signal 502 during the first active time periodform an active-pump PVP signal 502A, the values of the which includenoise artifacts from the operation of the pump 111. The noise artifactsof such active time segments inhibit PIVA and other related analysis, soit is useful to remove, replace, or adjust the active-pump PVP signal502A prior to further analysis. Additional second and third inactivetime segments associated with inactive-pump PVP signals 502I duringwhich the pump 111 is not operating are further illustrated betweentimes t₂ and t₃ and between times t₄ and t₅. An additional active timesegment associated with active-pump PVP signals 502A during which thepump 111 is operating is illustrated between times t₃ and t₄. Althoughthe active time segments and inactive time segments are illustrated asadjacent in time in the exemplary chart, some embodiments may includetransition periods that are neither part of any inactive time period norpart of any active time period.

FIG. 5B illustrates an exemplary cleaned PVP signal 508 comprising onlythe inactive-pump PVP signals 502I. The exemplary cleaned PVP signal 508may be generated by simply removing data values associated with theactive time segments, leaving gaps 506 in the cleaned PVP signal 508. Inorder to remove the active time segments, the analysis component 114 mayfirst identify one or more of either or both of active time segments orinactive time segments. In some embodiments, information from the pump111 (such as the pump control signal 504) may be used to identify activetime segments or inactive time segments. In preferred embodiments,however, the analysis component 114 may identify the active timesegments or inactive time segments based upon the values of the PVPsignal 502. The analysis component 114 may identify the active timesegments or inactive time segments based upon magnitudes of the valuesor changes in the values of the PVP signal 502, as discussed furtherbelow.

Once generated, the cleaned PVP signal 508 may be directly analyzedaccording to the methods described herein, or the PVP signal 508 may befurther adjusted further prior to transformation to the frequencydomain. For example, the cleaned PVP signal 508 may be adjusted toremove the gaps 506 by aligning the inactive-pump PVP signals 502I to bepartially overlapping based upon a periodicity of the inactive-pump PVPsignals 502I. As another example, the cleaned PVP signal 508 may beadjusted to fill the gaps 506 with estimated values based upon theinactive-pump PVP signals 502I, as illustrated in FIG. 5C. Although theinactive-pump PVP signal 502I associated with a single inactive timesegment may be sufficient for frequency-domain analysis of patientstatus metrics if the inactive time segment is of sufficiently longduration, the inactive time segments may be of too short durations toallow accurate analysis. In such instances, combining a plurality ofinactive-pump PVP signals 502I over a corresponding plurality ofinactive time segments facilitates further analysis by providing moredata for evaluation. Even when individual inactive time segments aresufficiently long to allow frequency analysis, the accuracy may beimproved by adding additional data values associated with additionalinactive time segments.

FIG. 5C illustrates an exemplary adjusted PVP signal 510 comprising theinactive-pump PVP signals 502I and estimated PVP signals 502E to fillthe gaps 506. The values of the estimated PVP signals 502E may beestimated based upon the values of the inactive-pump PVP signals 502I ofthe cleaned PVP signal 508, as discussed further below. By filling thegaps 506 with the estimated PVP signals 502E, the resulting adjusted PVPsignal 510 may be better suited for some types of further analysis.Specifically, the adjusted PVP signal 510 represents a comprehensivetime series of data without noise artifacts from operation of the pump111, which may be analyzed without further adjustment for the effects ofpumping. It should be noted that the adjusted PVP signal 510 may beobtained from the measured PVP signal 502 alone, without reference toextrinsic data regarding the pump 111. Thus, extrinsic data regardingtimes of pump operation (e.g., time periods of pump operation) orcharacteristics of pump operation (e.g., pump speed, pump volume, ormodels of noise artifacts generated by the pump) is not needed in orderto generate the adjusted PVP signal 510.

Although FIG. 5C illustrates the estimated PVP signals 502E as onlyfilling the gaps 506 created by removing the active-pump PVP signals502A, some embodiments may include estimating the entire adjusted PVPsignal 510. In such embodiments, both the active-pump PVP signals 502Aand the inactive-pump PVP signals 502I may be replaced with estimatedPVP signals 502E to generate the adjusted PVP signal 510. Although suchapproach may reduce accuracy of the analysis in some respects byreplacing measured values of the inactive-pump PVP signals 502I withestimated values of the estimated PVP signals 502E, the approach maybetter facilitate further analysis by eliminating discontinuities at theboundaries between the active and inactive time segments (i.e., at timest₁, t₂, t₃, and t₄). In yet further embodiments, discontinuities may beaddressed by adjusting values of one or more of the inactive-pump PVPsignals 502I or the estimated PVP signals 502E that occur near theboundaries between the active and inactive time segments to smooth thetransitions. In any case, the active-pump PVP signals 502A are excludedfrom the adjusted PVP signal 510 and are replaced with the estimated PVPsignals 502E.

FIG. 6 illustrates a flow diagram of an exemplary pressure signalfiltering method 600 for removing noise artifacts related to operationof a medical device from a signal corresponding to the PVP of thepatient 102. The filtering method 600 may be implemented by theevaluation unit 118 to obtain, filter, and analyze a PVP signal todetermine a patient status metric. Noise artifacts from the operation ofa pump 111, other fluid source 110, or similar medical device canobscure ordinary PVP measurements during operation. For analyticalmethods such as PIVA, these noise artifacts must be removed or otherwiseaddressed prior to further processing in order to obtain accuratemetrics. In contrast with other methods of addressing device noiseartifacts, the filtering method 600 identifies and removes signal valuesassociated with active time segments from a PVP signal containing activetime segments of device operation and inactive time periods when thedevice is inactive. To do this, a time-domain PVP signal (such as PVPsignal 502) is obtained and processed to remove signal values associatedwith active time segments (such as active-pump PVP signals 502A) togenerate a filtered time-domain PVP signal (such as cleaned PVP signal508 or adjusted PVP signal 510). The filtered time-domain PVP signal maythen be transformed to the frequency domain and analyzed according tothe methods discussed herein to determine one or more patient statusmetrics.

The filtering method 600 begins by obtaining a time-domain PVP signalfrom measurements associated with pressure in a peripheral vein of thepatient 102 (block 602). The time-domain PVP signal may be directlygenerated by the pressure sensor 112 or may be derived from sensormeasurements, as discussed elsewhere herein. As also described elsewhereherein, the time-domain PVP signal may be obtained by monitoring thepressure sensor 112 or by accessing a stored PVP data signal. In someembodiments, the evaluation unit 118 may monitor and record data from atransducer to generate the time-domain PVP signal. The time-domain PVPsignal may include one or more of each of the following: (i) active timesegments during which the pump 111 is operating (i.e., actively pumping)and (ii) inactive time segments during which the pump 111 is notoperating (i.e., not actively pumping). The active time segments and theinactive time segments may alternate periodically or aperiodically.Although the pump 111 may be configured to operate in such a manner asto inherently produce both active and inactive time segments during theordinary course of use, the active time segments are periods duringwhich the pump 111 is generating noise artifacts by active operation,while the inactive time segments are periods during which the pump 111is not generating significant noise artifacts by passive or inactiveoperation (e.g., a rest period between cyclical pumping). To enablefurther analysis of the measured PVP, the evaluation unit 118 mayidentify and filter the active and inactive time segments.

The filtering method 600 may, therefore, identify values of thetime-domain PVP signal associated with the active time segments or theinactive time segments (block 604). The evaluation unit 118 mayautomatically identify the active time segments, the inactive timesegments, or both the active and inactive time segments based upon thevalues of the time-domain PVP signal. In preferred embodiments, theevaluation unit 111 may identify the time segments based solely uponanalysis of the time-domain PVP signal, without reference to additionalextrinsic information regarding the characteristics or operating statusof the pump 111 that is not contained in or derived from the time-domainPVP signal (e.g., previously determined pump operating parameters or acontrol signal controlling operation of the pump). Thus, the evaluationunit 118 may identify the time segments in the same manner, regardlessof the characteristics, configuration, or settings of the pump 111, andwithout requiring adjustments to or further configuration of theevaluation unit 118. In various embodiments, the evaluation unit 118 mayautomatically identify the time segments based upon magnitudes of thevalues of the time-domain PVP signal or based upon changes in magnitudesof the values of the time-domain PVP signal. The values may be analyzedindividually or in sets containing a plurality of values, according toone or more set metrics applied to the sets.

For individual values of the time-domain PVP signal, each of a pluralityof values may be compared to one or more threshold levels to determinewhether the value is associated with a time within an active timesegment or an inactive time segment. For example, values above an upperthreshold level may be identified as being associated with an activetime segment, or values below a lower threshold level may be identifiedas being associated with an inactive time segment. The values may begrouped based upon such comparisons to identify the active and inactivetime segments. While the upper and lower threshold levels may beidentical in some embodiments, they may be distinct levels in otherembodiments. When distinct, an indeterminate range exists in whichvalues cannot be assigned to either active time segments or inactivetime segments. Such indeterminate values may be further analyzed basedupon the surrounding time segments to determine whether suchindeterminate values belong to active time segments, inactive timesegments, or transition time segments. In some embodiments, outliervalues may be discarded or identified as being part of an active orinactive time segment based upon values surrounding such outlier valuesin time (i.e., preceding and following the outlier value). In order toremove the noise artifacts more completely, transition time segments maybe treated as active time segments in some embodiment for the purpose ofgenerating the filtered time-domain PVP signal.

For sets of values of the time-domain PVP signal, each set may beanalyzed using one or more set-metrics to determine whether the set isassociated with an active or inactive time segment. In preferredembodiments, each set contains values of the time-domain PVP signal thatare adjacent in time, forming a time series of values of the PVP signal.Thus, each set is associated with a set-specific time period andcomprises values associated with times within the set-specific timeperiod. The set-specific time periods of the sets may cover fixeddurations or may be of variable durations, and the set-specific timeperiods may be overlapping or non-overlapping. In a preferredembodiment, subsequent windows of data overlap each other by 50%. Thesets may include sampled values from the time-domain PVP signal, or thesets may include all values of the time-domain PVP signal associatedwith times within the set-specific time periods of the correspondingsets. In certain embodiments, the set-specific time periods can benon-overlapping but adjacent sets covering all time periods within ananalysis time period for which data is available for an uninterruptedduration of the time-domain PVP signal, such that each value of thetime-domain PVP signal during the analysis time period is in exactly oneof the sets. Thus, the active or inactive time segments may beidentified as collections of one or more sets by identifying the sets asbeing associated with either active or inactive time segments.

To identify a set as being associated with an active or inactive timesegment, the values of the time-domain PVP signal within the set may beevaluated using one or more set-metrics. The set-metrics may includefunctions that determine an average value, a maximum value, a minimumvalue, a distance between maximum and minimum values, an average changebetween values (or absolute value thereof), a variance of the set, oranother metric of the values in the set. Once a set-metric has beendetermined by evaluating the values of a set, the set-metric may becompared against a set-threshold level associated with the set-metric toidentify the set as being associated with an active time segment or aninactive time segment. For example, sets may be identified as beingassociated with active time segments when a set-metric is above theset-threshold level for the set-metric or being associated with inactivetime segments when a set-metric is below the set-threshold level for theset-metric.

In some embodiments, the set-metric may determine changes between valueswithin a set, such as a rate of change. Such rate of change may be anaverage rate of change, a maximum rate of change, or other measures ofchanges between the values. Set-metrics regarding changes or rates ofchange between values may be used to determine beginning times or endingtimes of active or inactive time segments by comparison against athreshold associated with the starting or stopping of active pumping.PVP may spike when the pump 111 begins active pumping at the start of anactive time segment and drop precipitously when the pump 111 stopsactive pumping at the end of the active time segment. Thus, large andrapid changes in the values of the time-domain PVP signal may be used toidentify beginnings or ends of the active and inactive time segments.For example, the beginning time of an active time segment may beidentified by determining a change or rate of change set-metric is abovea pumping start threshold, and the beginning time of an inactive timesegment may be identified by determining a change or rate of changeset-metric is below a pumping stop threshold. Active and inactive timesegments may then be identified based upon such beginning or endingtimes.

Once the active and inactive time segments are identified in thetime-domain PVP signal, the evaluation unit 118 may generate a filteredtime-domain PVP signal (block 606). The filtered time-domain PVP signalmay be an adjusted PVP signal 510 having estimated PVP signals 502E (asillustrated in FIG. 5C) or may instead be a cleaned PVP signal 508 thatsimply removes the active-pump PVP signals 502A (as illustrated in FIG.5B). The filtered time-domain PVP signal is generated based upon thetime-domain PVP signal and excludes the values of the time-domain PVPsignal associated with the active time segments. In contrast to othermethods that attempt to correct for pump noise artifacts by estimatingand removing the noise artifacts themselves, the filtering method 600estimates what the PVP signal would have been if the pump 111 had notbeen operating.

In certain embodiments, there is a communication aspect between the pump111 and the analysis component 114, which allows for smart interactionsregarding removal of noise signal. Specifically, for example, if thepump 111 identifies an internal issue, such as a pump malfunction, thepump 111 may inform the system of the internal issue. Responsive tobecoming aware of the pump's internal issue, the system may takeprecautionary measures, such as ceasing measurements for a period oftime, displaying a warning, or any other related precautionary measure.

As illustrated above in the cleaned time-domain PVP signal 508, thefiltered time-domain PVP signal may be generated by removing the valuesassociated with one or more identified active time periods from thetime-domain PVP signal. Where the time-domain PVP signal comprises asequential time series of discrete values, the filtered time-domain PVPsignal may be generated by removing those values identified by theircorresponding times as falling within an active time segment, therebyleaving one or more sequential time series of discrete valuescorresponding to times falling within inactive time segments. In someembodiments, the filtered time-domain PVP signal may further be adjustedor normalized before further analysis. For example, the remaining valuesassociated with inactive time segments may be stitched together to avoidhaving gaps in the filtered time-domain PVP signal (such as the gaps506). To do this, corresponding values within each of a plurality ofinactive time segments may be identified, and the inactive time segmentsmay be combined by aligning these identified corresponding values. Thus,the beginning of one inactive time segment may be aligned with the endof the preceding inactive time segment such that the cycles (i.e., thecardiac cycles of the patient) are aligned. This may further requireremoving or blending overlapping values of one or both of the inactivetime segments to produce an uninterrupted filtered time-domain PVPsignal.

As illustrated above in the adjusted PVP signal 510, the filteredtime-domain PVP signal may alternatively be generated by replacing thevalues associated with the one or more identified active time periodswith substitute values. The substitute values are determined based uponthe values associated with one or more inactive time segments in thetime-domain PVP signal. Thus, the filtered time-domain PVP signal may begenerated by combining the values of the time-domain PVP signalassociated with the inactive time segments with the substitute valuesfor the active time segments to produce an uninterrupted signal or timesequence of values. In some embodiments, the substitute values may begenerated by estimating values for the active time segments based upon amodel determined by regression analysis, principal component analysis,or similar techniques. The model parameters may be estimated by ordinaryleast squares regression on the values associated with the inactive timesegments. In preferred embodiments, however, the model may be estimatedby least cubes regression on the values associated with the inactivetime segments, which produces improved results for PVP signals undermany circumstances. In some embodiments, the substitute values may beadjusted near the boundaries between the active and inactive timesegments in order to smooth the transitions between the inactive timesegment values and the substitute values. In further embodiments, thesubstitute values may be estimated for both the active and inactive timesegments, in which case the values of both may be replaced by theestimated substitute values to generate the filtered time-domain PVPsignal. Such filtered time-domain PVP signals may be beneficial in someinstances, inasmuch as such signals avoid breaks or discontinuities ofthe signal at boundaries between the active and inactive time segments.

Once the filtered time-domain PVP signal has been generated for one ormore time periods (such as the evaluation windows discussed above), theevaluation unit 118 may further analyze the data by generatingfrequency-domain PVP data from the one or more filtered time-domain PVPsignals (block 608). In a manner similar to that discussed elsewhereherein, a time-frequency transform (such as FFT) may be applied to thefiltered time-domain PVP signals to generate frequency-domain PVP dataas a representation of PVP in the frequency domain after filtering toremove the noise artifacts from operation of the pump 111. Suchfrequency-domain PVP data may be generated as frequency distributionsassociated with the one or more filtered time-domain PVP signals. Byusing the filtered time-domain PVP signal to generate thefrequency-domain PVP data, PVP can be analyzed for patients connected tocyclically operating pumps 111, despite the noise artifacts generated bypump operation. If the pump 111 is directly connected to the patient'scirculatory system, the methods described herein enable analysis atrates of operation up to the point at which the inactive time segmentsbecome too short and too infrequent for reliable filtering (e.g.,approximately 250 cc/minute for most adult patients with typical heartrate and respiratory rate using an infusion pump such as the SIGMASpectrum® infusion system produced by Baxter International Inc.). Thefrequency-domain data may then be further analyzed to determine one ormore patient status metrics (block 610). Such frequency-domain analysismay include analysis of the frequencies or magnitudes of frequency peaks(FN), as discussed in further detail elsewhere herein. In someembodiments, this may include comparing frequency-domain PVP data todetermine changes in patient status metrics.

In certain embodiments, hardware utilized to practice the inventionincludes a multi-transducer setup to find forward and backwardspropagating waves (e.g., IV vs pump 111 signal) based on phase andamplitude differences. For example, the system may include a pluralityof sensors, such as two or more sensors. A pump-proximate sensor may belocated closer to the pump 111; a second sensor may be located furtherfrom the pump 111, such as near the peripheral location on the patient.By identifying noise at the pump-proximate sensor, prior to identifyingit at the second sensor, the system may affirmatively identify that thenoise was pump-generated, as opposed to patient generated. In certainembodiments, additional hardware includes a dampening chamber and/ortubing, configured to subtract or limit non-physiologic signals prior todigital analysis. For example, an in-line signal dampener. In certainembodiments, additional hardware includes multiple sensors with adampening chamber disposed there between, to enable subtraction of pumpsignal. In certain embodiments, additional sensors may measure otherphysiological characteristics, such as heart rate and/or respiratoryrate, as an input to high harmonics, for determining fluid status.

FIG. 7 illustrates an exemplary PIVA comparison method 700 foridentifying changes in a patient status based upon comparison offrequency-domain representations of PVP signals associated withdifferent times. The PIVA comparison method 700 may be implemented bythe evaluation unit 118 and the response unit 116 to determine andrespond to changes in patient status between time periods. For example,the evaluation unit 118 may determine and compare frequency-domainrepresentations of PVP based upon electronic pressure signals receivedduring multiple time periods to determine changes in patient metrics,such as blood pressure, blood volume, respiration, position or movement,or systemic vascular resistance. Specifically, the evaluation unit 118may compare relative or absolute magnitudes associated with frequencies(F_(N)) of peaks (P_(N)) in the frequency distributions determined foreach time period to identify changes in patient status that may be usedby the response unit 118 to determine and implement response actions.

The exemplary method 700 begins by obtaining a first frequencydistribution associated with a first time period (block 702) and asecond frequency distribution associated with a second time period(block 704). Each of the first and second frequency distributions may begenerated as the frequency-domain data corresponding to the plurality ofdata values from the PVP data signal by the method 300 or the filteringmethod 600, as described above. The first and second time periods maycorrespond to first and second evaluation windows, as discussed above,each evaluation window being associated with a plurality of data valuessampled or received by the evaluation unit 118. The data values for eachof the first and second evaluation windows may be stored in a volatileor non-volatile memory until needed by the evaluation unit 116 togenerate the frequency distributions, as discussed above. Alternatively,the frequency distributions or information associated therewith (e.g.,frequency peaks and associated magnitudes) may be stored directly forcomparison. In some embodiments, the first and second frequencydistributions may be frequency-domain representation of the PVP signalsfrom the sensor 112 for time periods of fixed duration beginning attimes separated by a predetermined interval. For example, the method 700may be implemented on a rolling basis (i.e., periodically or when newPVP data becomes available) during real-time monitoring of the patient102 by comparing the magnitudes of frequency peaks (F_(N)) of frequencydistributions generated for first and second evaluation windows duringpatient monitoring. The first and second periods may be partiallyoverlapping, adjacent in time, or separated by an intervening period.

The evaluation unit 116 may next identify one or more peaks of interestfor determining the patient status (block 706). The peaks of interestmay be identified in either or both of the first and second frequencydistributions. In some instances, the one or more peaks of interestedmay be determined based upon peaks (P_(N)) in a baseline frequencydistribution generated for the patient 102, which may be the firstfrequency distribution or an additional prior frequency distribution.The baseline frequency distribution may, for example, be determinedprior to scheduled surgery to establish a baseline for later patientstatus monitoring. The peaks of interest may be identified based uponthe associated frequencies (F_(N)), such as by identifying therespiratory frequency (F₀) or the heart rate frequency (F₁). In someembodiments, the peaks of interests may include a plurality of suchpeaks, such as the peaks (P₂) and (P₃) associated with the firstharmonic frequency (F₂) and the second harmonic frequency (F₃). Undersome conditions, not all peaks of interest may be identifiable in bothfrequency distributions. For example, during an acute failure of thecirculatory system, systemic vascular resistance may markedly decreaseand peaks associated with the harmonic frequencies (F₂, F₃, . . . F_(N))may not be discernible. Thus, the peaks associated with the harmonicfrequencies (F₂, F₃, . . . F_(N)) may be identifiable in the firstfrequency distribution but not in the second frequency distribution.Nonetheless, a change in magnitude of the frequency distributions at theharmonic frequencies (F₂, F₃, . . . F_(N)) may be determined bycomparison of the first and second frequency distributions.

Based upon the identified one or more peaks of interest, the evaluationunit 116 may further determine a patient status (or a change in patientstatus) by a comparison of the first and second frequency distributions(block 708). Determining the patient status may include a comparison ofthe magnitudes associated with the same one or more frequencies (F_(N))between the first and second frequency distributions, a comparison ofthe values of a function of a plurality of magnitudes associated withfrequencies between the first and second frequency distributions (e.g.,a comparison of ratios of peak magnitudes), a comparison of frequencies(F_(N)) associated with one or more peaks (P_(N)) between the first andsecond frequency distributions (e.g., a change in the respiratoryfrequency or heart rate frequency), or a comparison of other metricsassociated with patient status. In some embodiments, the patient statusmay be determined based upon a change in a metric beyond a thresholdlevel. For example, a decrease in the magnitude associated with theheart rate frequency (F₁) in the second frequency distribution below 80%of the corresponding magnitude associated with the heart rate frequency(F₁) in the first frequency distribution may indicate hypovolemia in thepatient 102. As another example, a decrease in the ratio of themagnitude associated with the first harmonic frequency (F₂) to themagnitude associated with the heart rate frequency (F₁) between thefirst and second frequency distributions beyond a predeterminedthreshold may indicate hypervolemia or hypovolemia, depending uponwhether and how the magnitude associated with the heart rate frequency(F₁) changes. Comparisons of particular interest are discussed infurther detail elsewhere herein.

A comparison involving one or more of the harmonic frequencies (F_(H))is of particular interest regarding patient hemodynamic state or bloodvolume. Because the frequency distribution values associated withharmonic frequencies (F_(H)) are more sensitive to changes in bloodvolume than the values associated with the heart rate frequency (F₁),monitoring changes in the values associated with harmonic frequencies(F_(H)) may provide an earlier or clearer indication of patienthemodynamic state. For example, a sharp increase or decrease in themagnitude of the value of the frequency distribution associated with thefirst harmonic frequency (F₂) (or other harmonic frequency) may be morepronounced than the corresponding change in the values associated withthe heart rate frequency (F₁) in the same patient at the same time.Thus, blood volume metrics may be generated using the harmonicfrequencies (F_(H)). Such metrics may be determined as functions of theharmonic frequencies (F_(H)), ratios of the frequency values of theharmonic frequencies (F_(H)), magnitudes associated with the harmonicfrequencies (F_(H)), ratios of the magnitudes associated with theharmonic frequencies (F_(H)), or changes in any of these. Such changesmay be measured against a baseline or against a previously determinedvalue at a fixed interval in time prior to the current values. In someembodiments, the frequencies or magnitudes associated with the harmonicfrequencies (F_(H)) may be compared against other relevant values, suchas frequencies or magnitudes associated with the respiratory ratefrequency (F₀) or the heart rate frequency (F₁). For example, one ormore harmonic frequencies (F_(H)) may be normalized by comparisonagainst the heart rate frequency (F₁). Such normalized value may bedetermined as a ratio of the magnitudes and may be used as a bloodvolume metric to evaluate the hemodynamic state of the patient 102.Other similar blood volume metrics based at least in part upon thefrequency and magnitude values of the one or more harmonic frequencies(F_(H)) may be determined and used to evaluate the hemodynamic state ofthe patient 102 in various embodiments.

Once patient status has been determined, the response unit 116 maydetermine whether a response is required and cause any required responseto be implemented (block 510). Additionally, or alternatively, theevaluation unit 118 or the response unit 116 may cause an indicator ofthe determined patient status to be stored or presented via the monitor120 (block 510). If the response unit 116 determines a response isrequired, the response unit 116 may further determine one or moreresponses that are appropriate to address the identified patient status.Such responses may include generating an alarm or other warning that thepatient status is abnormal, which may include information regarding thepatient condition. An alarm or warning may be presented via the monitor120 or may be communicated to another device for presentation. The alarmor warning may include a recommendation of one or more actions to takein response to the patient status. For example, the recommendation mayinclude an adjustment to a fluid therapy for the patient 102, which mayinclude a recommendation to administer one or more vasopressors orvasodilators. Such recommendation may be determined by the response unit116 as part of the required response. In some embodiments, this mayinclude sending an electronic communication to a user device (e.g., aworkstation or mobile device used by a physician, nurse, or technicianto monitor patient condition).

The responses may similarly include controlling the fluid source 110 toadjust fluid flow to the patient 102. The fluid source 110 may becontrolled to increase or reduce the rate of fluid flow to the patient102, including starting or stopping fluid flow. In some embodiments, theresponse may include controlling the fluid source 110 (or a deviceconnected thereto) to administer one or more drugs to the patient 102.For example, the fluid source 110 may be controlled to administer one ormore vasopressors or vasodilators in a fluid delivered to the peripheralvein 108 via the IV tube 104 and venous access device 106. Where thefluid source 110 includes a pump, the response may include controllingthe operation of the pump, such as by increasing or decreasing pumpspeed, flow rate, or mode of operation, as well as starting or stoppingthe pump. In some embodiments, the fluid source 110 may be controlled toadminister a quantity of a drug to the patient 102 via the fluid. Forexample, the fluid source 110 may be controlled to add a quantity of thedrug to the fluid. Additional embodiments of specific analysis andresponse methods utilizing the PIVA system 100 are further describedelsewhere herein in greater detail.

The PIVA Module

The PIVA system 100 may perform several signal filtering and signalprocessing steps (e.g., to remove the noise artifacts from aphysiological signal, to perform FFT on a physiological signal, tocalculate a PIVA score as a corollary to pulmonary capillary wedgepressure, etc.). In an embodiment, the PIVA system 100 performs thesesteps, and others, via the PIVA module 800. Although the PIVA module 800is described with reference to the block diagram illustrated in FIG. 8,it will be appreciated that many other configurations and methods ofperforming the acts associated with PIVA module 800 may be used. Forexample, the order of some of the blocks may be changed, certain blocksmay be combined with other blocks, and some of the blocks described maybe optional.

As illustrated in FIG. 8, the PIVA module 800 includes a noise module802, a signal quality index module 804, a pulse rate module 806, an FFTmodule 808, and a respiratory rate module 810.

The PIVA module 800 receives at least one input. For example, the PIVAmodule 800 may receive a digital signal from an analog-digitalconverter. The digital signal may be representative of a patientphysiological parameter, such as a patient's peripheral intravenouspressure. It should be appreciated that many other physiologicalparameters are contemplated, such as other invasive venous pressures,invasive arterial pressures, noninvasive venous pressure, noninvasivearterial pressures, etc. In an example, the digital signal is derivedfrom a medical device, such as a pressure transducer that is in fluidcommunication with the patient's vein.

Likewise, the PIVA module 800 delivers outputs. For example, the PIVAmodule 800 may output a signal quality index (SQI) related to the PIVAsystem 100, a respiratory rate (RR) of the patient, a pulse rate (PR) ofthe patient, and a PIVA score of the patient.

Noise Module

Responsive to receiving the digital signal, the PIVA module 800 mayperform filtering and processing. In an embodiment, the digital signalis processed via noise module 802 to eliminate noise artifacts, such asthose associated with the operation of a pump. For example, the noisemodule 802 may perform forward-backward slope calculations to identifysegments of the digital signal where there is noise. In an embodiment,noise module 802 performs several processing steps to eliminate noiseartifacts from a signal. In an embodiment, processing includes cascadedstack processing. This may advantageously provide for real-timeprocessing and efficient decimation of recurrent feature calculations,block processes, filtering, etc.

More specifically, the noise module 802 may evaluate the digital signal,identify a point where the positive slope of the signal is greater thana particular threshold (e.g., a signal spike), and characterize thisportion of the digital signal as a noise start point. This may generallybe characterized as slope based burst detection. Similarly, the noisemodule 802 may evaluate the digital signal, identify a point where thenegative slope of the digital signal is less than a particular threshold(e.g., a signal drop) and characterize this portion of the signal as anoise end point. Slopes may be calculated by taking the derivative ofthe digital signal.

In an example, noise module 802 implements a sliding window stack sizethat is sufficient for local parameter estimation (e.g., for real-timeprocessing). Noise module 802 determines the slope window size on eachside of a peak within a particular stack (e.g., peaks typicallyassociated with signal noise). For example, to calculate the slopes:

ForwardSlope=S{X[p-wdex]−X[p]}/(p-wdex)

BackwardSlope=S{X[p]−X[p-wdex]}(p-wdex)

Preferably, spacing between slope windows is tested for a wide range ofpump rates. Noise module 802 may also calculate a symmetry point betweenthe forward and backward slopes. The symmetry point may infer thepeak-noise location. In an embodiment, high slope and/or high amplitudenoise is detected.

The slope based burst detection is an adaptive input signal conditioningprocess, which provides for real-time noise cancellation. For example,noise module 802 identifies a noise-start and a noise-stop time, removesthe signal between the noise-start time and noise-stop time (e.g.,concatenates the signal). In other words, once a noise segment isidentified (e.g., the signal portion between the noise start point andthe noise end point), the noise module 802 may delete the segment fromthe digital signal (e.g., to produce a concatenated or segmentedsignal).

Likewise, for example, noise module 802 may also perform mirror-matchedfiltering to fill in gaps of the concatenated signal. More specifically,the signal range between the sign-adjusted forward and backward slopesgreater than a threshold (e.g., the signal noise region) is replaced bya mirror image of the symmetrically split adjacent regions. In oneembodiment, mirror-matched filtering involves filling in each gap fromthe front (e.g., from the noise end point) and the back (e.g., from thenoise start point). In a different embodiment, mirror-matched filteringinvolves filling in the gap using prior digital signal data, which isstored in a memory (e.g., buffer memory). For example, noise module 802retrieves buffer stack memory and fills synthetic data from the forwardand/or reverse direction of the signal. In an embodiment, buffer andwindow size are optimized for pump rates from 25 Hz to 250 Hz.

Processing performed by noise module 802, including slope based burstdetection to identify noise and subsequent processing, advantageouslyeliminates noise artifacts from signals. For example, with pump rates upto 250 mL per hour, noise module 802 has at least 0.74 seconds betweenpumping intervals; this is necessary for patients with low pulse rate toacquire appropriate signals. Subsequent processing, in a preferredembodiment, includes deletion of noise signals and adjustment of FFTmagnitudes based on a ratio of deleted noise signal, described ingreater detail herein. In an alternate embodiment, processing includesdeletion of noise signals and mirror-matching, such as matching ofexisting signals to “fill” deleted signals by flipping existing signalsacross a y-axis. Preferably, the end result is a cleaned signal that haseliminated noise artifacts. After the noise module 802, the PIVA module800 may perform additional processing on the cleaned signal.

Signal Quality Index Module

In an embodiment, the cleaned signal may be processed via signal qualityindex module 804 to obtain the SQI related to the PIVA system 100. Forexample, the signal quality index module 804 may include autocorrelationof the cleaned signal (e.g., the waveform), which may includedetermination of both zero-crossing mean, and standard deviation of zerocrossings. Zero crossings analysis may advantageously be used tocalculate SQI. Responsive to processing via the signal quality indexmodule 804, the PIVA module 800 may output the SQI.

More specifically, determining signal quality includes analyzing theautocorrelation of the signal. Autocorrelation may include putting theraw digital signal on top of itself (e.g., the raw digital signal on topof the cleaned signal). When there is a statistical spread in zerocrossings, approximately the same as the zero crossing rate, the signalmay be an unusable signal. For example, when the standard deviation ofthe zero crossings is similar to the number of zero crossing events, thesignal may be unusable. To calculate Signal Quality:

ZCSD=Autozerocross−zerocrossSD

Signal Quality=sqrt(abs(ZCSD)/(autozerocross+zerocrossSD))

This calculated signal quality value may be displayed as a signalquality percentage and delivered as SQI.

In an embodiment, if the signal quality is determined to be “low”quality, a monitor in communication with PIVA module 800 will display aspecific graphical user interface. For example, the monitor may indicate“Poor Signal Quality.” Similarly, the monitor may include signal qualitytroubleshooting recommendations. For example, the monitor may suggest to(1) check patient status, (2) check IV catheter for displacement, air,and kinks, (3) check pump rate to ensure it is below 250 mL per hour,(4) check for patient movement, (5) identify that the device may not becompatible for use with more than one infusion pump, and (6) flush andconfirm that the IV catheter draws back.

Pulse Rate Module

In a related embodiment, the cleaned signal may be processed via pulserate module 806 to obtain the PR of the patient. For example, the pulserate module 806 may determine top spectral peaks of the cleaned signalusing two-sided slope detection. Responsive to processing via the pulserate module 806, the PIVA module 800 may output the PR.

More specifically, processing includes cascaded stack processing. Thismay advantageously provide for real-time processing and efficientdecimation of recurrent feature calculations, block processes,filtering, etc.

In an embodiment, pulse rate module 806 implements autocorrelationprocessing for periodicity determination to compute pulse rate (alsoreferred to herein as heart rate or HR). For example, pulse rate module806 uses an 8192 sample block size, which may also be the stack buffersize that is processed in reverse order for correct periodicityfeatures. Pulse rate module 806 may implement selectable overlappingintervals. As an example, the default interval may be a one secondinterval with 500 samples. Pulse rate module 806 may computeautocorrelation for lags (e.g., 0 to 4000, related to periodicities upto 8 seconds). Pulse rate module 806 may compute peak-associated lags(e.g., 17 peak-associated lags), using forward and backward slopedetection as previously described. Pulse rate module 806 may filterzero-crossing periods and standard deviations. Pulse rate module 806 maycompute filtered mean-spacing between sub-harmonics. Pulse rate module806 may compute an HR estimate.

In an embodiment, pulse rate module 806 implements spectral processing,such as FFT, to determine HR. For example, pulse rate module 806 uses a8192 point block size, which may preferably include no window function.Pulse rate module 806 may determine spectral peaks by forward andbackward slope technique. Pulse rate module 806 may use the zerothharmonic as a partial HR estimate. The spectral magnitude associatedpeaks, which are independent of autocorrelation inferred repetitionrates, are accordingly identified. Spectral magnitude peakidentification may be used to calculate pulse rate (e.g., one peak) aswell as volume index (e.g., multiple peaks). Discussion of spectralmagnitude peak identification is included in the FFT Module sectionbelow.

In a related embodiment, pulse rate module 806 implements FFT to refineHRs previously determined via autocorrelation. In this embodiment, theHR initially calculated via autocorrelation is a partial HR estimate.

In another embodiment, pulse rate module 806 further computes heart ratevariability (HRV) and HRV variability. For example, because pulse ratemodule 806 is performing peak detection over sliding windows, pulse ratemodule 806 can determine how data changes or varies, and thus determineHRV and HRV variability.

FFT Module

In a related embodiment, the cleaned signal may be processed via FFTmodule 808 to obtain the PIVA score of the patient. For example, the FFTmodule 808 may perform spectral analysis on the cleaned signal to obtainmagnitudes. These FFT magnitude spectra may be used to calculate a PIVAscore (as described in greater detail below). Responsive to processingvia the FFT module 808, the PIVA module 800 may output the PIVA score.

More specifically, FFT module 808 is used to identify spectral magnitudepeaks, which are subsequently used to calculate volume index (e.g.,multiple peaks). In an embodiment, processing includes cascaded stackprocessing. This may advantageously provide for real-time processing andefficient decimation of recurrent feature calculations, block processes,filtering, etc.

FFT module 808 implements spectral processing to identify spectralmagnitude peaks. In an embodiment, identification of the individualmagnitude peaks includes: utilizing the max found change in forwardbackward slope that is assisted by the guidance of the autocorrelationpulse rate, the magnitude peaks of the Fourier transformation are found.

These individual magnitudes (e.g., F₁, F₂, F₃, etc.) are then used in anon-linear equation to calculate volume index.

For example, as previously mentioned, subsequent to performingtransformation, the FFT module 808 may identify the peaks (e.g., P₁, P₂,P₃) of the corresponding frequencies (e.g., F₁, F₂, F₃). Theseindividual peaks (e.g., P₁, P₂, P₃) corresponding to the variousfrequencies, such as the heart rate frequency F₁, the first harmonic ofthe heart rate frequency F₂, and the second harmonic of the heart ratefrequency F₃ may then be used in an equation to calculate a PIVA Score.PIVA Score, representative of a patient's fluid status, is also acorollary for pulmonary capillary wedge pressure. Because pulmonarycapillary wedge pressure is an indicator of fluid status (e.g.,hypervolemia or hypovolemia), the PIVA Score, likewise, isrepresentative of a patient's fluid status.

In an embodiment, the equation to calculate PIVA Score is represented bythe following:

${{PIVA}\mspace{14mu} {Score}} = {{c_{3}{\tanh ( \frac{g_{0} + {g_{1}{mag}^{f\; 1}} + {g_{2}{mag}^{f\; 2}} + {g_{3}{mag}^{f\; 3}}}{2} )}} + {c_{2}{\tanh ( \frac{h_{0} + {h_{1}{mag}^{f\; 1}} + {h_{2}{mag}^{f\; 2}} + {h_{3}{mag}^{f\; 3}}}{2} )}} + {c_{1}{\tanh ( \frac{i_{0} + {i_{1}{mag}^{f\; 1}} + {i_{2}{mag}^{f\; 2}} + {i_{3}{mag}^{f\; 3}}}{2} )}} + c_{0}}$

Each of c₀, c₁, c₂, c₃, g₀, g₁, g₂, g₃, h₀, h₁, h₂, h₃, i₀, i₁, i₂, andi₃ are constants. For example, c₀, c₁, c₂, and c₃ may be catheter gaugeconstants. In this example, the user may enter a specific gauge, relatedto the catheter to be used, via the user interface communicating withPIVA module 800; PIVA module 800 subsequently provides c₀, c₁, c₂, andc₃ to FFT module 808, based on the user entry.

Each of mag^(f1), mag^(f2), and mag^(f3) represents the individualmagnitudes of each of the respective frequencies (e.g., F₁, F₂, F₃).These magnitudes are also commonly referred to herein as peaks offrequencies. For example, mag^(f1) may also be referred to as peak P₁herein, associated with heart rate frequency F₁. Similarly, for example,mag^(f2) may also be referred to as peak P₂ herein, associated withfirst harmonic frequency F₂. Similarly, for example, mag^(f3) may alsobe referred to as peak P₃ herein, associated with second harmonicfrequency F₃. For example, and with reference to FIG. 4B, P₁, referredto in the PIVA Equation as mag^(f1), is the magnitude of the heart ratefrequency (F₁), P₂, referred to in the PIVA Equation as mag^(f2), is themagnitude of a first harmonic frequency (F₂), and P₃, referred to in thePIVA Equation as mag^(f3), is the magnitude of a first harmonicfrequency (F₃)

FFT module 808 calculates the PIVA score, which is unitless. In arelated embodiment, PIVA system 100 displays the PIVA score (e.g., viamonitor 120). By calculating PIVA score, a patient's fluid status may bereadily determined (e.g., hypovolemia, hypervolemia, or euvolemia).Preferably, the calculated PIVA score has an agreement with pulmonarycapillary wedge pressure of ±8 mmHg with limits of agreement of 95%confidence interval.

In an embodiment, additional peak magnitudes corresponding to variousfrequencies (e.g., P₄ corresponding to F₄, a third harmonic frequency)may also be used in calculating the PIVA score (e.g., implementingadditional constants as well) for greater accuracy in calculation.

Additional ways to determine the relationship between PIVA Score andpatients' pulmonary capillary wedge pressure include the fitting of datawas evolutionary algorithms to optimize a low complexity and low errorsolution as well as neural network mapping of the data with a trainingand validation set using nodes of hyperbolic tangential functions tocreate non-linear relationships between values.

In a related embodiment, FFT module 808 performs an algorithmic approachto calculating volume index. For example, FFT module 808 performs aninitial least squares approach to analyze the individual magnitudes(e.g., F₁, F₂, F₃, etc.) and subsequently calculates a best-fit forvolume index. The best-fit for volume index can, alternatively, becharacterized as a best-fit for pulmonary capillary wedge pressure.Responsive to generating the best-fit, the FFT module 808 may use thebest-fit for subsequent iterations to calculate volume index. In thisexample, subsequent iterations may allow for non-linear calculations ofthe PIVA score.

Respiratory Rate Module

In an embodiment, the cleaned signal may also be processed viarespiratory rate module 810 to obtain the RR of the patient. Forexample, the respiratory rate module 810 may filter the cleaned signalthrough a high-pass filter. In an embodiment, the respiratory ratemodule 810 may further perform recursive discrete analysis (e.g., sin()+cos ( ) and computation of related ArcTan (y/x)) to determine RR.Recursive discrete analysis may be used, for example, to further“smooth” the cleaned signal, such that the resultant signal is a singleperiodic wave. In other words, by smoothing the signal to eliminate allhigher-level frequencies, the single periodic wave signal is analogousto respiratory rate. In alternate embodiments, the respiratory ratemodule 810 performs alternate processing to determine RR, such ascalculating the frequency of the cleaned signal via FFT, comparing thecleaned signal to known data sets via template-matching, or any otherrelated means of analysis. Responsive to processing via the respiratoryrate module 810, the PIVA module 800 may output the RR.

Determining respiratory rate may include using a digital linear FMdiscriminator based on differential phase angle filtering. Prior to thisdetermination, pulse rate is calculated, as described above. The pulserate data is then replicated. The respiratory rate module 810 applies adigital high-pass filter to the signal. For example, the high-passfilter isolates the respiratory rate frequency range and permits fittingof data to extract the respiratory rate.

More particularly, the input signal is high-passed filtered, for maximumvolatility detection. The respiratory rate module 810 performs recursivefiltering of quadrature, such as Cosine(2*PI*n*k) and Sin(2*PI*n*k), andthen calculates the filtered ArcTan( ) of filtered quadrature terms. Therespiratory rate module 810 computes the derivative of the filteredArcTan( ) angle. In an embodiment, the respiratory rate module 810further performs light filtering of the derivative of the filteredArcTan( ) angle. The respiratory rate module 810 may then estimatedominant baseband frequency. Multiplying the estimation by 60 provides arespiration rate on a per minute basis.

The PIVA System

FIG. 9 illustrates a block diagram of an exemplary PIVA system 900,including the PIVA module 800 previously described herein. In additionto PIVA module 800, PIVA system 900 may include processor 902 and memory904, running on PIVA module 800. For example, PIVA module 800 mayinclude one or more physical processors 902 communicatively coupled toone or more memory devices 904.

Physical processor, such as processor 902, refers to a device capable ofexecuting instructions encoding arithmetic, logical, and/or I/Ooperations. In one illustrative example, a processor may follow VonNeumann architectural model and may include an arithmetic logic unit(ALU), a control unit, and a plurality of registers. In an example, aprocessor may be a single core processor, which is typically capable ofexecuting one instruction at a time (or process a single pipeline ofinstructions), or a multi-core processor, which may simultaneouslyexecute multiple instructions. In another example, a processor may beimplemented as a single integrated circuit, two or more integratedcircuits, or may be a component of a multi-chip module (e.g., in whichindividual microprocessor dies are included in a single integratedcircuit package and hence share a single socket). A processor may alsobe referred to as a central processing unit (CPU). Memory device, suchas memory device 904, refers to a volatile or non-volatile memorydevice, such as RAM, ROM, EEPROM, or any other device capable of storingdata. Local connections, including the connections between processor 902and memory device 904, may be provided by one or more local buses ofsuitable architecture, for example, peripheral component interconnect(PCI).

Likewise, PIVA system 900 may include sensor 906 and monitor 908. Forexample, PIVA module 800 may be in communication with each of sensor 906and monitor 908. Communication may be wired and/or wireless (e.g., WiFi,Bluetooth, etc.). In an example, sensor 906 is the pressure sensor 112described in greater detail above. In an example, monitor 908 is themonitor 120 described in greater detail above. In an embodiment, PIVAmodule 800 is physically located within monitor 908.

Likewise, PIVA system 900 may include database 910 and cloud 912. Forexample, PIVA module 800 may be in communication with each of database910 and cloud 912. Communication may be wired and/or wireless (e.g.,WiFi, Bluetooth, etc.). In an example, database 910 includes electronicmedical records stored on a hospital network. In an example, cloud 912includes a remote storage location, which may be used to storephysiological data and/or device information (e.g., PIVA module 800performance statistics, software updates, etc.).

In an embodiment, the PIVA system 900 displays an updated volume indexvia monitor 908 every 60 seconds. Preferably, the PIVA score used tocompute the volume index has an agreement with pulmonary capillary wedgepressure of ±8 mmHg with limits of agreement of 95% confidence interval.

In an embodiment, the PIVA system 900 displays an updated pulse rate viamonitor 908 every 10 seconds. Preferably, the pulse rate has anagreement with the heart rate of ±10 beats per minute with limits ofagreement of 95% confidence interval.

In an embodiment, the PIVA system 900 displays an updated respirationrate via monitor 908 every 10 seconds. Preferably, the respiration ratehas an agreement with the respiratory rate of ±5 breaths per minute withlimits of agreement of 95% confidence interval.

In an embodiment, the PIVA system 900 operates in conjunction with anexternal medical device. For example, the PIVA system 900 operates inconjunction with an infusion pump operating at rates of 0 to 250 mL perhour. In a related embodiment, the PIVA system 900 utilizes noisecancellation (e.g., via noise module 802) to remove the pump signal fromthe detected waveform (e.g., the digital signal).

In an embodiment, the PIVA system 900 displays the volume index (e.g.,PIVA score) the pulse rate, and the respiratory rate when the signalquality is adequate. For example, signal quality is adequate when thesignal quality index indicates that signal quality is adequate. Ifsignal quality is inadequate, the PIVA system 900 may indicate that thesignal is of “low” quality and/or cease displaying physiological values(e.g., PR, RR, PIVA score, etc.) so long as the signal quality remainsinadequate.

The PIVA system 900 may include other additional features. In anembodiment, the PIVA system 900 includes a power supply. The powersupply may be wired to an external source and/or may have internalpower, such as a Li-ion battery. In an embodiment, the PIVA system 900includes one or more speakers (e.g., a primary speaker and a backupspeaker). The speakers may be configured to sound alarms if necessary.

FIG. 10 illustrates another example of signal processing via process1000. In various embodiments, any of PIVA system 100, PIVA system 900,and master controller 1009 (as detailed below) may perform process 1000.In an embodiment, process 1000 may be implemented in conjunction withprocess 800. In a different embodiment, example 1000 is an individualprocess, distinct from process 800. Although the process 1000 isdescribed with reference to the block diagram illustrated in FIG. 10, itwill be appreciated that many other configurations and methods ofperforming the acts associated with process 1000 may be used. Forexample, the order of some of the blocks may be changed, certain blocksmay be combined with other blocks, and some of the blocks described maybe optional.

As illustrated in FIG. 10, process 1000 may include several individualfunctions, including interference cancellation logic function 1002,frequency magnitude detection function 1004, pulse rate detectionfunction 1006, and respiratory rate detection function 1008. Each ofthese functions may be performed by or operate with the mastercontroller 1009 (e.g., a processor).

The interference cancellation logic function 1002 may include a sensorinput (block 1010). For example, a sensor input (e.g., a pressuretransducer signal) at a particular frequency (e.g., 500 Hz) may bereceived as the sensor input. The sensor input may be an analog and/or adigital signal. In a preferred embodiment, the sensor input is digital.A 149 point FIR filter output (e.g., a low-pass filter) may be added toa slope array (block 1012). For example, the filter may be added to theslope array that is representative of the digital signal received as thesensor input. The filter may, in one example, eliminate all frequenciesof the signal that exceed 16 Hz. It should be appreciated, however, thatother filters and/or frequency limits could be implemented.

Forward and backward slope detection may be performed (block 1014). Theinterference cancellation logic function may determine if theforward/backward slope exists (block 1016). If a forward/backward slopeexists, such that pump noise is effectively identified, detected datapoints in the slope detection array are removed (block 1018), frequencymay be adjusted (block 1022), and a cubic-fit is applied to removediscontinuity (block 1024). Specifically regarding the cubic-fit, forexample, a spline fit may be performed on the data, such that the datais represented by a continuous line going forward. Alternatively, if theforward/backward slope does not exist, input data is used as the value(block 1020).

The current point (e.g., current point of the signal) is compared to ahistogram, and rejected if the current point is outside the confidencebounds (block 1026). For example, if the current point is beyond theconfidence bounds, this indicates there may be a problem with the data.The output line from the cubic-fit step (block 1024) is added to anautocorrelation array, and the sample is shifted by one (block 1030). Inan example, this process, including addition to the autocorrelationarray and shifting the sample is occurring 500 times per second. In anexample, the process repeats with the 149 point FIR filter output beingadded to the slope array (block 1012). In a different example, theprocess 1000 continues on to the next function.

The frequency magnitude detection function 1004 includes determining ifthe sample count is greater than an FFT trigger value (block 1032). Inan example, the sample count required is at least 8192 samples.

If the sample count is greater than the FFT trigger value (block 1032),a FFT of the most recent 8192 points is performed (block 1034). Themagnitude of the FFT output is calculated (block 1036). Once themagnitude of the FFT output is calculated, this magnitude may further beadjusted based on sample count (block 1037).

Specifically with regards to adjusting calculated magnitudes based onsample count (block 1037), a full sample window may include 8192samples. If the entire sample window is “noise free,” such that no pumpnoise was identified during forward/backward slope detection (blocks1016, 1018) the sample window will include 8192 samples. Alternatively,however, if a portion of the sample window includes noise, such as pumpnoise, the window will include fewer than 8192 samples. For example, if25% of the sample window includes noise, 25% of the 8192 samples will beremoved by the interference cancellation logic function 1002 (block1018). In other words, this particular sample will only include 6144samples, which is 75% of the 8192 samples that were taken. Thus, thecalculated magnitude based on sample count (block 1037) relates to 6144samples, and not 8192 samples. To adjust the calculated magnitude,process 1000 adjusts the frequency domain based on a ratio between thereal sample set and the total sample set. For example, as illustratedbelow, adjusted frequency can be calculated via:

$F_{adjusted} = \frac{F_{calculated}}{( \frac{{sample}\mspace{14mu} {count}}{8192} )}$

With the previous example provided, the initially calculated frequencies(block 1036) would be divided by (6144/8192), or more simply would bedivided by 0.75. In other words, the initial frequency is divided by thepercentage of signal in a sample window (e.g., 8192 samples) that didnot include noise. Through this adjustment, the calculated frequenciescan be adjusted to account for missing samples, such as those deletedfor being identified as noise (block 1018); adjusted frequencies willprovide the frequency magnitude detection function 1004 with moreaccurate values for additional processing, such as for PIVA volume indexcalculation (block 1046).

The maximum peak is identified to estimate the pulse rate (block 1038).Pulse rate is calculated (block 1040). A peak search is conducted forthe harmonics of the first frequency (e.g., F₁) (block 1042). Forexample, peaks may be searched by proportional differences from they-axis; a first peak could be expected at 1×, a second peak could beexpected at 2×, etc. A spectral magnitude calculation is performed fromthe pulse rate estimation (block 1044). FFT quality may be calculated(block 1045). For example, the height the peak FFT signal (e.g., F₀) isdivided by the baseline FFT signal. Baseline FFT signal is a non-peaksignal, typically determined by determining amplitude of the signal afar distance from the peaks. When the peak FFT signal is much higherthan the baseline FFT signal (e.g., 50× to 100×, or higher), the FFTsignal has defined peaks and FFT Quality is sufficient. For example, FFTQuality should be 50 or higher. A PIVA Volume Index (e.g., PIVA score)is calculated (block 1046) and the FFT trigger value is updated (block1048). The PIVA Volume Index may be sent to the master controller 1009.Because the PIVA Volume Index is calculated, the frequency magnitudedetection function 1004 may also be generally characterized as a PIVAScore function.

If the sample count is not greater than the FFT trigger value (block1032), the process 1000 continues on to the next function. Likewise,responsive to performing the spectral magnitude calculation from thepulse rate estimation (block 1044), the process 1000 may continue on tothe next function.

The pulse rate detection function 1006 includes determining if thesample count is greater than an autocorrelation trigger value (block1050).

It is additionally valuable to determine the signal quality index (SQI).Specifically, if the sample count is greater than the autocorrelationtrigger value (block 1050), an inverse FFT magnitude of the most recent8192 points is performed (block 1052). For example, the inverse FFTeffectively re-creates the time domain signal. In an example, inverseFFT magnitude provides the time domain signal (e.g., forautocorrelation). The real output is scaled to the square-root of themagnitude (block 1054). In an embodiment, the inverse FFT is scaled forautocorrelation purposes. The minimum, maximum, and mean ofautocorrelation are identified (block 1056), for example, to obtain thezero point. A cubic fit of 4000 points is performed (block 1058). In anexample, this cubic fit of 4000 points (e.g., 8 seconds of signal) isintended to estimate heart rate. Forward/backward slope calculations forminimum and maximum slope pairs are performed (block 1060), for example,to identify peaks. Pulse rate is calculated (block 1062). In an example,calculated pulse rate is equal to the number of slope pairs. Signalquality index (SQI) is assessed, to determine if SQI is greater than aparticular threshold (e.g., SQI>70). If SQI is not greater than thethreshold (block 1064), the measured data is treated as “zero” and sentto the master controller 1009 as such. In an example, no information isdisplayed while SQI is not greater than the threshold.

Alternatively, if SQI is greater than the threshold (block 1064), aweighted average of pulse rates is calculated (block 1066), and mayfurther be compared to the autocorrelation pulse rate. Theautocorrelation trigger value is updated (block 1068). The calculatedpulse rate may be sent to the master controller 1009. In an example,calculating the weighted average of pulse rates (block 1066) includesreceiving input of the pulse rate calculated by the frequency magnitudedetection function 1004 (block 1040).

If the sample count is not greater than the autocorrelation triggervalue (block 1050), the process 1000 continues on to the next function.

The respiratory rate detection function 1008 includes determining if thesample count is greater than a respiratory rate trigger value (block1070). Respiratory rate detection function 1008 performs recursivediscrete analysis (e.g., sin( )+cos ( ) and computation of relatedArcTan (y/x)) to “smooth” the cleaned signal, such that the resultantsignal is a single periodic wave (block 1072. In other words, bysmoothing the signal to eliminate all higher-level frequencies, thesingle periodic wave signal is analogous to respiratory rate. Signalquality is assessed, to determine if SQI is greater than the particularthreshold (e.g., SQI>70). Again, if SQI is not greater than thethreshold (block 1076), the measured data is treated as “zero” and sentto the master controller 1009 as such. In an example, no information isdisplayed while SQI is not greater than the threshold.

Alternatively, if SQI is greater than the threshold (block 1076),respiratory rate is calculated (block 1078) and the respiratory ratetrigger value is updated (block 1080). The calculated respiratory ratemay be sent to the master controller 1009.

SQI is calculated using zero crossing statistics derived from theautocorrelation (e.g., the number of zero crossing events and standarddeviation of zero crossing events) (block 1082). For example,calculating SQI (e.g., at block 1064, at block 1076, etc.) may take intoaccount pulse rate with respect to the number of slope pairs calculatedby the pulse rate detection function 1006 (block 1062). The rollingaverage of SQIs missed over time is also calculated (block 1084). If theSQI rolling average trigger is reached (block 1086), an SQI error issent to the master controller 1009. For example, if enough time haspassed without any data, a threshold is reached. In various embodiments,the threshold may trigger a troubleshoot guide, additional warnings,and/or alarms. Likewise, if the SQI rolling average trigger is notreached (block 1086), an SQI no error is sent to the master controller1009.

As used in this specification, including the claims, the term “and/or”is a conjunction that is either inclusive or exclusive. Accordingly, theterm “and/or” either signifies the presence of two or more things in agroup or signifies that one selection may be made from a group ofalternatives.

The many features and advantages of the present disclosure are apparentfrom the written description, and thus, the appended claims are intendedto cover all such features and advantages of the disclosure. Further,since numerous modifications and changes will readily occur to thoseskilled in the art, the present disclosure is not limited to the exactconstruction and operation as illustrated and described. Therefore, thedescribed embodiments should be taken as illustrative and notrestrictive, and the disclosure should not be limited to the detailsgiven herein but should be defined by the following claims and theirfull scope of equivalents, whether foreseeable or unforeseeable now orin the future.

The invention is claimed as follows:
 1. A system for monitoring apatient using a measurement associated with a pressure within acirculatory system of the patient while the circulatory system of thepatient is connected to a pump, comprising: a pressure sensor includinga transducer disposed adjacent to or connected to a tube in fluidconnection with the circulatory system and configured to generate anelectronic signal associated with the pressure while the circulatorysystem of the patient is connected to the pump; and an evaluation unit,including a computer processor communicatively connected to the pressuresensor to receive the electronic signal and a memory storingnon-transitory computer-readable instructions that, when executed by thecomputer processor, cause the evaluation unit to: obtain a time-domainpressure signal comprising values of an electronic signal associatedwith the pressure from the transducer based upon a physical phenomenonassociated with the pressure of the patient over a sample period,wherein the sample period includes a plurality of time segments,including (i) one or more active time segments during which the pump isoperating and (ii) one or more inactive time segments during which thepump is not operating; identify a first plurality of the values of thetime-domain pressure signal associated with the one or more inactivetime segments and a second plurality of the values of the time-domainpressure signal associated with the one or more active time segments viaforward and backward slope detection of the time-domain pressure signal;delete the second plurality of values of the time-domain pressuresignal; generate a filtered time-domain pressure signal based upon thefirst plurality of the values and excluding the second plurality of thevalues; apply a transformation to the filtered time-domain pressuresignal to generate a frequency-domain pressure signal; adjust thefrequency-domain pressure signal, based on a ratio between a size of thesecond plurality of values of the time-domain pressure signal and atotal sample window of values of the time-domain pressure signal; anddetermine a patient status metric for the patient based upon theadjusted frequency-domain pressure signal.
 2. The system of claim 1,wherein the pump is a peristaltic IV pump.
 3. The system of claim 1,wherein the pump is configured to operate periodically, such that theone or more active time segments and the one or more inactive timesegments periodically alternate.
 4. The system of claim 1, wherein thetube is disposed between the patient and the pump such that a part ofthe pump is in fluid connection with the circulatory system of thepatient via the tube.
 5. The system of claim 4, wherein: the transducercomprises a pressure sensor disposed in fluid connection with aninterior of the tube; and the physical phenomenon associated with thepressure is a pressure within the interior of the tube.
 6. The system ofclaim 4, wherein the instructions further cause the evaluation unit to:determine whether the patient status metric indicates a condition of thepatient is abnormal; and adjust operation of the pump when the patientstatus metric indicates the condition of the patient is abnormal bychanging a rate of flow of a fluid from the pump into the circulatorysystem of the patient.
 7. The system of claim 1, wherein the executableinstructions further cause the evaluation unit to generate the filteredtime-domain pressure signal by, for each of one or more pairs of theactive time segments: identifying one or more corresponding valueswithin both of the active time segments of the pair; and combining theactive time segments of the pair by aligning the one or morecorresponding values within both of the active time segments of thepair.
 8. The system of claim 1, wherein the executable instructions thatcause the evaluation unit to generate the filtered time-domain pressuresignal include instructions that cause the evaluation unit to: estimatea third plurality of values as substitute values for the one or moreactive time segments, wherein the third plurality of values areestimated based upon the first plurality of values without reference tothe second plurality of values; and generate the filtered time-domainpressure signal by combining the first plurality of values for theinactive time segments and the third plurality of values for the activetime segments.
 9. The system of claim 8, wherein the third plurality ofvalues are estimated by performing at least one of regression analysis,forward-backward slope calculation, two-sided slope detection, andmirror matched filtering on at least the first plurality of values. 10.The system of claim 1, wherein the executable instructions that causethe evaluation unit to determine the patient status metric includeinstructions that cause the evaluation unit to: identify a plurality offrequencies associated with local maxima of the frequency-domainpressure signal; and determine the patient status metric based at leastin part upon at least one of the plurality of frequencies associatedwith the local maxima.
 11. The system of claim 1, wherein the patientstatus metric is a blood volume metric indicating one or more of thefollowing: hypovolemia, hypervolemia, or euvolemia.
 12. A device formonitoring a patient, comprising: a pressure sensor, including atransducer configured to monitor a physical phenomenon associated with apressure within a circulatory system of the patient while thecirculatory system of the patient is connected to a pump; and anevaluation unit, including a computer processor communicativelyconnected to the pressure sensor and a memory storing non-transitoryexecutable instructions that, when executed by the computer processor,cause the evaluation unit to: obtain a time-domain pressure signalcomprising values of an electronic signal associated with the pressurereceived from the transducer of the pressure sensor over a sampleperiod, wherein the sample period includes a plurality of time segments,including (i) one or more active time segments during which the pump isoperating and (ii) one or more inactive time segments during which thepump is not operating; identify a first plurality of the values of thetime-domain pressure signal associated with the one or more inactivetime segments and a second plurality of the values of the time-domainpressure signal associated with the one or more active time segments viaforward and backward slope detection of the time-domain pressure signal;delete the second plurality of values of the time-domain pressuresignal; generate a filtered time-domain pressure signal based upon thefirst plurality of the values and excluding the second plurality of thevalues; apply a transformation to the filtered time-domain pressuresignal to generate a frequency-domain pressure signal; adjust thefrequency-domain pressure signal, based on a ratio between a size of thesecond plurality of values of the time-domain pressure signal and atotal sample window of values of the time-domain pressure signal; anddetermine a patient status metric for the patient based upon theadjusted frequency-domain pressure signal.
 13. The device of claim 12,wherein: the time-domain pressure signal comprises a first time seriesof discrete values; the filtered time-domain pressure signal comprises asecond time series of discrete values; and the second time seriescontains at least one segment of a sequential plurality of values withinthe second time series that are equivalent to a corresponding segment ofa sequential plurality of corresponding values within the first timeseries.
 14. The device of claim 12, wherein the executable instructionsthat cause the evaluation unit to generate the filtered time-domainpressure signal include instructions that cause the evaluation unit to:estimate a third plurality of values as substitute values for the one ormore active time segments, wherein the third plurality of values areestimated based upon the first plurality of values without reference tothe second plurality of values; and generate the filtered time-domainpressure signal by combining the first plurality of values for theinactive time segments and the third plurality of values for the activetime segments.
 15. The device of claim 12, wherein adjusting thefrequency-domain pressure signal includes dividing magnitudes of thefrequency-domain pressure signal by a quotient of the second pluralityof values and the total sample window of values.
 16. A method ofmonitoring a patient using a measurement associated with a pressurewithin a circulatory system of the patient while the circulatory systemof the patient is connected to a pump, comprising: monitoring, by atransducer, a physical phenomenon associated with the pressure of thepatient over a sample period, wherein the sample period includes aplurality of time segments, including (i) one or more active timesegments during which the pump is operating and (ii) one or moreinactive time segments during which the pump is not operating;obtaining, by a processor of an evaluation unit, a time-domain pressuresignal comprising values of an electronic signal associated with thepressure from the transducer based upon the monitored physicalphenomenon over the sample period; identifying, by the processor of theevaluation unit, a first plurality of the values of the time-domainpressure signal associated with the one or more inactive time segmentsand a second plurality of the values of the time-domain pressure signalassociated with the one or more active time segments via forward andbackward slope detection of the time-domain pressure signal; deletingthe second plurality of values of the time-domain pressure signal;generating, by the processor of the evaluation unit, a filteredtime-domain pressure signal based upon the first plurality of the valuesand excluding the second plurality of the values; applying, by theprocessor of the evaluation unit, a transformation to the filteredtime-domain pressure signal to generate a frequency-domain pressuresignal; adjusting the frequency-domain pressure signal, based on a ratiobetween a size of the second plurality of values of the time-domainpressure signal and a total sample window of values of the time-domainpressure signal; and determining, by the processor of the evaluationunit, a patient status metric for the patient based upon the adjustedfrequency-domain pressure signal.
 17. The method of claim 16, whereingenerating the filtered time-domain pressure signal includes removingthe one or more active time segments from the time-domain pressuresignal.
 18. The method of claim 16, wherein generating the filteredtime-domain pressure signal includes: estimating a third plurality ofvalues as substitute values for the one or more active time segments,wherein the third plurality of values are estimated based upon the firstplurality of values without reference to the second plurality of values;and generating the filtered time-domain pressure signal by combining thefirst plurality of values for the inactive time segments and the thirdplurality of values for the active time segments.
 19. The method ofclaim 16, wherein the third plurality of values are estimated byperforming at least one of regression analysis, forward-backward slopecalculation, two-sided slope detection, and mirror matched filtering onat least the first plurality of values.
 20. The method of claim 16,wherein adjusting the frequency-domain pressure signal includes dividingmagnitudes of the frequency-domain pressure signal by a quotient of thesecond plurality of values and the total sample window of values.